Hasil untuk "Photography"

P. D. Madan Kumar, Rajeshwari Selvam, S. Savitha et al.

Background: Intraoral photography through mobile phones is an important tool in teledentistry, particularly for early detection of oral diseases in resource-limited settings. However, the diagnostic utility of these images hinges on their quality, which is influenced by technical metadata, environmental conditions, and operator variability. Objective: The objective is to evaluate the impact of metadata on intraoral image quality by comparing expert ratings with ImageJ metrics and identifying optimal settings for high-quality capture. Materials and Methods: This cross-sectional study analyzed 30 intraoral images captured with a Samsung Galaxy M15 5G. Ten dental experts rated each image on six quality parameters using a 3-point Likert scale. Objective measurements were obtained via ImageJ, and metadata were extracted using ExifTool. Statistical analysis examined correlations between metadata, objective metrics, and expert ratings. Results: Expert ratings showed good consistency (Cronbach’s α = 0.771) and substantial agreement (Fleiss’ κ = 0.68, 95% confidence interval: 0.62–0.74, P < 0.001). Objective analysis showed mean brightness of 113.00 ± 16.50, contrast 31.77 ± 4.43, and sharpness 2.66 ± 0.58. ISO and brightness values were significantly correlated with image brightness and red, green, and blue (RGB) intensities (P < 0.001), aligning with expert assessments. Brightness value significantly predicted red and blue channel intensities, while no metadata parameter predicted contrast, sharpness, or noise. Ideal image quality corresponded to ISO 20–25, brightness 9.0–10.0, and RGB values of R: 135–150, G: 110–125, and B: 100–115. Conclusion: ISO and brightness metadata significantly influence both objective and subjective image quality. Machine learning can leverage metadata and pixel data to predict diagnostic usability and enable real-time quality control in large-scale screenings.

Niccolò Giuseppe Armogida, Francesca Angelone, Parisa Soltani et al.

Background/purpose: Traditional caries detection relies on visual and radiographic analysis. While deep learning has been applied to classify caries extent, no studies classify caries depth using radiomic features in intraoral photographic images. This study evaluated a radiomics-based approach with machine learning (ML) to classify caries extent and depth, traditionally assessed via radiographs, using intraoral photographs. Materials and methods: Standardized intraoral photographs were taken with a Nikon D7500 and Macro Flash MF-R76. Only images of healthy teeth or carious lesions were included. Images were resized, segmented with Labelme, and classified using ICDAS and E-D scales. Data augmentation increased sample size. Radiomic features were extracted for each color channel using Pyradiomics. Feature selection methods (AUC-ROC, ReliefF, LASSO, backward selection) were applied within 5-fold cross-validation to prevent bias. ML classifiers (LDA, k-NN, SVM, NNET) evaluated accuracy, sensitivity, and specificity. Model explainability assessed feature influence via partial dependence plots, residual analysis, and break–down profile. Results: NNET with backward selection achieved high accuracy (87.6%–95.4%). Sensitivity and specificity ranged from 61.5% to 93% and 73%–90%, respectively. Green and red channels significantly impacted predictions, with texture features being critical. The red channel's greater impact reflects its ability to mimic near-infrared light transillumination, enhancing contrast between healthy and decayed tissue. The blue channel had lesser influence, but combined RGB channels yielded the best accuracy. Conclusion: Radiomics enables caries depth classification from intraoral photographs, offering a non-invasive, cost-effective alternative to radiographs. This approach could revolutionize diagnostics by reducing reliance on invasive radiological techniques, using accessible and affordable equipment.

Mathew L. Brust, Charles Bomar, Alexander J. Harman

The last update to the Wisconsin county-level distribution records for Wisconsin short-horned grasshoppers (Orthoptera: Acrididae and Romaleidae) was in 2005. Since then, the first author has done periodic collecting and photography in Wisconsin and has acquired a significant number of new county records. In addition, new online resources such as BugGuide and iNaturalist have become useful and mostly reliable sources of data. Using this combination of sources, we have compiled 99 new county records, increasing the number of county records by at least 20% for the following species: Dendrotettix quercus (Packard) (9 to 14), Dichromorpha viridis (Scudder) (14 to 23), Melanoplus fasciatus (Walker) (11 to 17), Melanoplus scudderi (Uhler) (3 to 5), Melanoplus viridipes Scudder (23 to 33), Melanoplus walshii Scudder (20 to 27), Stethophyma lineata (Scudder) (3 to 4), and by over 50% for Melanoplus punctulatus (Scudder) (10 to 30). In addition, a species new to Wisconsin, Melanoplus gracilis (Bruner), was photographed in three counties between 2020 and 2024. These examples suggest that some grasshopper species are likely overlooked in the state, particularly those associated with woodlands.

Katharina Storck, Julia Eufinger, Sebastian Kotz et al.

The integration of three-dimensional (3D) cameras into clinical practice for pre-operative planning and post-operative monitoring of rhinoplasties remains controversial. However, this technology offers the advantage of capturing the 3D surface without exposing patients to potentially harmful radiation. Continuous assessment allows the follow-up of swelling patterns, cartilage alignment, and bone remodeling. The primary objective of our study was to quantify changes in nasal structure before and after rhinoplasty by using 3D photography. Our study cohort consisted of 29 patients who underwent open structural rhinoplasty. We used the Artec Space Spider camera to acquire a total of 103 3D images. We collected pre-operative and at least two or three post-operative follow-up scans, which were taken one, three, and six months after surgery. We evaluated paired scans that included various time intervals to improve our understanding of swelling behavior and to ensure an objective analysis of changes. Eleven specific anatomical landmarks were identified for measurement. Two independent raters determined the distances between these landmarks over time. The calculation of intraclass correlation coefficients showed low inter-rater variability. Statistically significant changes over time (<i>p</i> < 0.05) were observed for various anatomical landmarks, including soft tissue nasion, soft tissue orbitale right, soft tissue maxillofrontale left, soft tissue maxillofrontale right, nasal bridge, and nasal break point. Conversely, no significant changes (<i>p</i> > 0.05) were observed in the measurements of soft tissue orbitale left, pronasale, subnasale, alare right, or alare left. A visual assessment was conducted using surface distance maps. The results indicate that the complete decrease in swelling takes at least 6 months or even longer. Additionally, 3D photography can provide an objectively comparable analysis of the face and external contours. Furthermore, it allows for a comparison of external contours and therefore pre- and post-operative differences.

Miguel Marquez, Giacomo Balistreri, Roberto Morandotti et al.

Single-shot 2-dimensional optical imaging of transient phenomena is indispensable for numerous areas of study. Among existing techniques, compressed ultrafast photography (CUP) using a chirped ultrashort pulse as active illumination can acquire nonrepetitive time-evolving events at hundreds of trillions of frames per second. However, the bulky size and conventional configurations limit its reliability and application scopes. Superdispersive metalenses offer a promising solution for an ultracompact design with a stable performance by integrating the functions of a focusing lens and dispersive optical components into a single device. Nevertheless, existing metalens designs, typically optimized for the full visible spectrum with a relatively low spectral resolution, cannot be readily applied to active-illumination CUP. To address these limitations, here, we propose single-shot compressed ultracompact femtophotography (CUF) that synergically combines the fields of nanophotonics, optical imaging, compressed sensing, and deep learning. We develop the theory of CUF’s data acquisition composed of temporal–spectral mapping, spatial encoding, temporal shearing, and spatiotemporal integration. We also develop CUF’s image reconstruction via deep learning. Moreover, we design and evaluate CUF’s crucial components—a static binary transmissive mask, a superdispersive metalens, and a 2-dimensional sensor. Finally, using numerical simulations, CUF’s feasibility is verified using 2 synthetic scenes: an ultrafast beam sweeping across a surface and the propagation of a terahertz Cherenkov wave.

Wei Wei, Xue Zhou, Zhen Huang et al.

Abstract Various types of foreign fibres may be mixed in the planting, transportation, and production processes of cotton, which not only cause equipment to be out of control, but also leads to a decrease in the quality of cotton textile products and economic losses. The machine vision based detection method for cotton foreign fibres is widely used. Based on existing related research, we construct a classification dataset for cotton foreign fibres in practical scenarios, named the CF2113‐10 dataset. The authors design a basic foreign fibre classification network called CottonNet that balances performance and efficiency. The classification accuracy on the validation set reached 94.2%. In order to enhance the high‐level feature extraction ability, this paper improves the feature fusion method of residual networks and proposes CottonNet‐Res, which improves the classification accuracy to 95.1%. Finally, a classification model based on feature difference fitting, CottonNet‐Fusion, is proposed to address the classification problem of foreign fibre images sampled in complex environments. The classification accuracy of foreign fibre images sampled in ordinary scenes has improved to 97.4%, while the images sampled in complex environments maintain an accuracy of 90.3%.

Javad Ebrahimnejad, Alireza Naghsh, Hossein Pourghasem

Abstract This paper proposes a robust watermarking method against high‐density salt and pepper noise attacks. Automatic region of interest (ROI) detection, embedding encoded data, removing different densities of noise, data extraction by omitting, and labeling the noisy pixels of Region of Non‐Interest (RONI), and decoding the extracted data using ROI pixel information as a key, are various steps of the presented scheme. The automatic ROI detection method separates the RONI from ROI with four vertices of the smallest rectangle, for the embedding process. The encoded watermark data is embedded into the least significant bits of RONI in four neighbour pixels. Adaptive Removal of high‐density Salt and pepper Noise method can enhance image quality and reduce the effect of salt and pepper noise attacks. The embedded information is preserved from destruction if the host image is impaired through the power of robustness. The best results are obtained through the action of extracting the watermark from RONI pixels, utilizing the same ROI detection method. Omitting and labeling the noisy pixels of the RONI will ensure healthy extracted watermark data, leading to decreased Bit Error Rate (BER) values. Finally, these data are interpreted using the key of ROI pixels, and the watermark data is decoded and retrieved. Due to salt and pepper noise obliterating pixel bits and their corresponding transform coefficients in the transform domain, the spatial domain is employed to enhance robustness against such attacks. The results show the high performance of the presented scheme. The average BER value for five MRI databases in a 97% salt and pepper noise attack is 38.6.

Antonio Malvasi, Lorenzo E. Malgieri, Ettore Cicinelli et al.

Asynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson’s correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson’s correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, <i>p</i> < 0.001), AD and HSD (PC = 0.18, <i>p</i> < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.

Yixuan Xu, Xianbing Wang, Hua Zhang et al.

Abstract The Swin‐Transformer is a variant of the Vision Transformer, which constructs a hierarchical Transformer that computes representations with shifted windows and window multi‐head self‐attention. This method can handle the scale invariance problem and performs well in many computer vision tasks. In image retrieval, high‐quality feature descriptors are necessary to improve retrieval accuracy. This paper proposes a self‐ensemble Swin‐Transformer network structure to fuse the features of different layers of the Swin‐Transformer network, eliminating noise points present in a single layer, and improving the retrieval effect. Two experiments were conducted, one on the In‐shop Clothes Retrieval dataset and another on the Stanford Online Product dataset. The experiments showed that the proposed method significantly increased the retrieval effect of features extracted using Vision Transformer, surpassing previous state‐of‐the‐art image retrieval methods. In the second experiment, the feature map of the trained model was visualized, revealing that the improved network significantly reduces focus on some noise points and enhances focus on image features compared to the original network.

Shubham Rana, Salvatore Gerbino, Mariano Crimaldi et al.

This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing <i>Triticum aestivum</i> crop and <i>Raphanus raphanistrum</i> weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.

Blessing Adjeketa, Godfrey Enita, Kenneth Eni

This study examines and documents the performance rituals, aesthetics, and entertainment in Okpe Traditional marriage performance using still photographs. Traditional marriage performances in Nigeria serves as entertainment, cultural re-awakening, and as a means of connecting/ energising the relationship between the physical and the supernatural world. While some of these performances have attained visibility through literary documentations, others seek recognition. Okpe marriage performances are also looking for safe landing. This research investigates the requirement for the marriage rites and rituals, handing over ritual, welcoming and reciprocal formalities and procession in Okpe traditional marriage performance, and locate the aesthetics of Songs, language, dress and makeup, the performative roles of the traditional orators, other performers and the audiences were also examined. It interrogates these subject matters using ethnographic research and critical participant observation methods. Findings indicates that while the performance and ritual aesthetics in Okpe traditional marriage have continued to serve as source material for modern African drama, still photography is a ready tool for documentation of the myriad traditional performances across Africa.

Weihua Zhang, Siping Wei, Changsheng Wang et al.

Drivers show different characteristics in traffic oscillations. These differences reflect the driver’s driving style, which is an important part of traffic uncertainty. This paper deeply explores the driving characteristics in asymmetric driving behaviour and its influence on traffic flow characteristics. The aim is to improve the understanding of safe driving. Continuous vehicle trajectories under various traffic flow conditions in an expressway merging area are obtained by aerial photography. Image processing technology is used to extract the basic parameters of traffic flow and vehicle operating characteristic data. Based on the measured data, the driver’s response mode is subdivided into multiple sub-modes. On the basis of this study, the types and distribution of traffic hysteresis and the impact of asymmetric behaviour on merging area capacity are further revealed. The results show that the response coefficient will increase for 58.72 % drivers during the process of experiencing oscillation disturbance to rebalance. The traffic hysteresis caused by driver’s asymmetric following behaviour in an expressway merging area is generally positive. This reduces the bottleneck outflow rate of the merging area by about 7 % on average. This study has important practical significance in analysing the formation mechanism of traffic congestion and adopting effective protective measures.

Wuyue Zhan, Heng Yao

Abstract Since Joint Photographic Experts Group is a typical image compression standard, so the reversible data hiding research on Joint Photographic Experts Group has received a lot of attention. There are two main research methods in Joint Photographic Experts Group‐reversible data hiding: the method based on modifying DCT coefficients and the method based on variable length coding mapping. The disadvantage of the first one is that the visual loss is always inevitable during the DCT‐coefficient modification process, while the second method often has insufficient embedding capacity and excessive file expansion. In order to solve the above problems, this paper proposes a cascaded reversible data hiding scheme, which builds a distortion minimisation scheme based on the DCT coefficients, and then cascades a file expansion suppression scheme based on the variable length coding mapping. For a particular case during the payload distribution, in this paper, all the secret information is embedded into DCT coefficients with a distortion minimisation scheme, and then the auxiliary information generated in the previous process is embedded in the variable length coding sequence with a scheme to optimise the file size expansion. The experimental results demonstrate the superiority of the proposed method in high‐quality factor circumstances in terms of visual quality and file expansion.

Michela Gardini

The motif of the painted portrait, widely attested in the 19th century European literature, plays a crucial role in Jules Barbey d'Aurevilly’s works, characterized as a threshold object founded on the triad of absence, desire and phantasm. Like a game of mirrors, the portrait and the text reflect their intrinsic lack in each other, delivering the reader a disturbing face to face with death. A true isotopy of the supernatural chains itself to the portrait which, rather than appearing as the theater of memory supposed to resuscitate the past, appears as a fascinating object able to bewitch the characters. The spectrality which characterizes it transforms it into a haunted portrait filling the text with visions and spectres. In contrast to the democracy of portrait, introduced by photography, the author claims the aristocratic and sacred value of the painted image, conceived as a magical object or even a relic.

Changtong Zan, Baodi Liu, Weili Guan et al.

Abstract The objective of crowd counting is to learn a counter that can estimate the number of people in a single image. So far, most of the proposed work evaluates the crowd density by fitting the constructed density map corresponding to the sample. The performance of those algorithms depends on a large amount of carefully prepared data. However, a significant problem with crowd data sets is the difficulty of labeling. To address such a situation, utilizing object counting data in few‐shot scenes is considered and an efficient algorithm to extract the meta‐information is proposed, thus improving the accuracy and convergence rate of the crowd counting tasks. Specifically, the counting network is trained with only object counting tasks constructed on different domains during the meta‐training phase. Then, the meta‐counter is testing on crowd counting tasks in the meta‐testing stage. Experimentally, it is demonstrated that the above way improves the converge rate and accuracy of crowd counting tasks on three crowd counting datasets when meta‐training on ten‐type object counting tasks.

Yu Qin, Yi Sun, Wei Zhang et al.

Plateau pika (<i>Ochotona curzoniae</i>, hereafter pika) is considered to exert a profound impact on vegetation species diversity of alpine grasslands. Great efforts have been made at mound or quadrat scales; nevertheless, there is still controversy about the effect of pika. It is vital to monitor vegetation species composition in natural heterogeneous ecosystems at a large scale to accurately evaluate the real role of pika. In this study, we performed field survey at 55 alpine grassland sites across the Shule River Basin using combined methods of aerial photographing using an unmanned aerial vehicle (UAV) and traditional ground measurement. Based on our UAV operation system, Fragmentation Monitoring and Analysis with aerial Photography (FragMAP), aerial images were acquired. Plot-scale vegetation species were visually identified, and total pika burrow exits were automatically retrieved using the self-developed image processing software. We found that there were significant linear relationships between the vegetation species diversity indexes obtained by these two methods. Additionally, the total number of identified species by the UAV method was 71, which was higher than the Quadrat method recognition, with the quantity of 63. Our results indicate that the UAV was suitable for long-term repeated monitoring vegetation species composition of multiple alpine grasslands at plot scale. With the merits of UAV, it confirmed that pika’s disturbance belonged to the medium level, with the density ranging from 30.17 to 65.53 ha⁻¹. Under this density level, pika had a positive effect on vegetation species diversity, particularly for the species richness of sedge and forb. These findings conclude that the UAV was an efficient and economic tool for species monitoring to reveal the role of pika in the alpine grasslands.

محمد جاسم عبد العيساوي اشواق سعيد رديني

Praise be to Allaah Mahmoud every tongue, the idol of all time, which is not without his knowledge everywhere, not occupied like for like, most of all likes and peer, and picnic for wife and children, and carried out his reign in all the subjects, neither representative by the brains to think, nor fancy hearts photography, (is not nothing like Him, He is the All-Seer), and prayer and peace be upon the Messengers Muhammad and his family and companions and followed them in truth until the Day of Judgment. After….. Has spoken in this search for The inheritance of women before and after Islam legislative provisions which, because of its importance to show the right of women's rights, which began Msluba before Islam and was re-Islam, to see the vast difference between the two periods and how Islam took into account this important segment of the community but it's a particular community. We have divided the research into four sections include demands as follows: First topic: the two demands and we talked about the legacy of the two women before Islam and in some contemporary societies and then we had a comparison between them.The second topic: It also includes two demands Pena during which evidence of the legality of the legacy of women in Islam, and the wisdom of inheritance for women legislation. The third topic: We reviewed it in detail cases in which inherit their women and in all of those cases, and cases in which women withhold withholding decrease or withhold deprivation. The fourth topic: separated the cases in which women inherit the man, and cases in which women inherit less or more or equal to the man or woman does not inherit the man inherits. Then the finale.

Junichi Sugishita, Leigh G. Torres, Philip J. Seddon

Incidental fisheries bycatch is recognised as a major threat to albatross populations worldwide. However, fishery discards and offal produced in large quantities might benefit some scavenging seabirds. Here, we demonstrate an integrated approach to better understand the ecological ramifications of fine-scale overlap between seabirds and fisheries. As a case study, we examined whether foraging in association with a fishing vessel is advantageous for chick provisioning in terms of quantity of food delivered to chicks, in northern royal albatross (Diomedea sanfordi) at Taiaroa Head, New Zealand. Fine-scale overlap between albatrosses and vessels was quantified by integrating GPS tracking and Vessel Monitoring Systems (VMS). Meal size delivered to chicks was measured using custom-designed nest balances, and monitoring of attendance of adults fitted with radio transmitters was used in conjunction with time-lapse photography at the nest allowed us to allocate each feeding event to a specific parent. The combination of these techniques enabled comparison of meal sizes delivered to chicks with parental foraging trip durations with or without fishing vessels association. A total of 45 foraging trips and associated chick feeding events were monitored during the chick-rearing period in 2012. Differences in the meal size and foraging trip duration relative to foraging overlap with fisheries were examined using a linear mixed-effect model, adjusted for chick age. Our results, based on three birds, suggest that foraging in association with vessels does not confer an advantage for chick feeding for this population that demonstrated low rates of overlap while foraging. The integrated research design presented can be applied to other seabird species that are susceptible to bycatch, and offers a valuable approach to evaluate habitat quality by linking habitat use and foraging success in terms of total amount of food delivered to offspring.

Mariana de Cabo

In Charles Baudelaire’s «Tableaux parisiens» and Le Spleen de Paris, an eye-camera that takes pictures of reality operates in a latent way. This photographic metaphor allows the poet to capture the Parisian landscape to fix it in the positive of poetry. In this article we analyze Baudelairian poetics by an interdisciplinary perspective that combines the language of literature and photography. We will use as theoretical framework Philippe Hamon’s semiological approach and Walter Benjamin Marxist theory.

William Christenberry

In this abridged version of an illustrated lecture given at Emory University on February 26, 2007, artist William Christenberry introduces major themes in his work and presents examples from more than forty years of photography, drawing, painting, and sculpture centered upon his home state of Alabama.