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UP-TO-DATE, TECHNICALLY ACCURATE COVERAGE OF ESSENTIAL TOPICS IN IMAGE AND VIDEO PROCESSING This is the first book to combine image and video processing with a practical MATLAB®-oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation. The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB® and its Image Processing Toolbox with the start of a series of chapters with hands-on activities and step-by-step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation. Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB®. Extra features of this book include: * More than 30 MATLAB® tutorials, which consist of step-by-step guides toexploring image and video processing techniques using MATLAB® * Chapters supported by figures, examples, illustrative problems, and exercises * Useful websites and an extensive list of bibliographical references This accessible text is ideal for upper-level undergraduate and graduate students in digital image and video processing courses, as well as for engineers, researchers, software developers, practitioners, and anyone who wishes to learn about these increasingly popular topics on their own.

We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

\"Given the timely topic and its user-friendly structure, this book can therefore target a suite of users, from students to experienced researchers willing to integrate the science of image processing to strengthen their research.\" (Ethology Ecology & Evolution, 1 May 2013).

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel. We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408-1,117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

Counting and sizing large farmed fish such as tuna is often performed during their transfer from one net cage to another. Dual-frequency-identification sonar (DIDSON) provides an automated fish counting and sizing tool. However, its counter and sizer are not suitable for measuring farmed fish because of net movements due to currents and subsequent frequent image breakups. This paper presents a fully automated acoustic method to count and size farmed fish during fish transfer by using DIDSON imaging. The background is subtracted from the image after being stabilized by an image phase-only correlation method. The segmentation of the fish is obtained by tracing the edges with a contour tracing method. To prevent recounting the same fish, a Kalman filter algorithm was designed and adapted to predict fish movements. Automated counting was performed by analyzing the spatiotemporal trajectory of the track. The separated fish images were searched for and body length was obtained by summing down the centerline segments from the head to the tail of the fish. The proposed system was verified using farmed yellowtail, Seriola quinqueradiata (mean total length 83.1 cm) to obtain a sizing error of mean total length within 2.4 cm.

Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.

Colluvial soils, resulting from accelerated soil erosion, represent a significant part of the soil cover pattern in agricultural landscapes. Their specific terrain position makes it possible to map them using geostatistics and digital terrain modelling. A study of the relationship between colluvial soil extent and terrain and soil variables was performed at a morphologically diverse study site in a Luvisol soil region in Central Bohemia. Assessment of the specificity of the colluviation process with regard to profile characteristics of Luvisols was another goal of the study. A detailed field survey, statistical analyses, and detailed digital elevation model processing were the main methods utilized in the study. Statistical analysis showed a strong relationship between the occurrence of colluvial soil, various topographic derivatives, and soil organic carbon content. A multiple range test proved that four topographic derivatives significantly distinguish colluvial soil from other soil units and can be then used for colluvial soil delineation. Topographic wetness index was evaluated as the most appropriate terrain predictor. Soil organic carbon content was significantly correlated with five topographic derivatives, most strongly with topographic wetness index (TWI) and plan curvature. Redistribution of the soil material at the study site is intensive but not as significant as in loess regions covered by Chernozem. Soil mass transport is limited mainly to the A horizon; an argic horizon is truncated only at the steepest parts of the slope.

A non-destructive Vis-NIR spectroscopy (400-1000 nm) method was developed to evaluate the loss of freshness of sliced and commercially packaged cooked ham and turkey ham without any sample manipulation. The spectra were recorded at 0, 30, 40, and 60 days using a camera, spectral filter (400-1000 nm) and a halogen floodlighting system which had been were developed and calibrated for the purpose. Physico-chemical, biochemical, and microbiological properties such as pH, total volatile basic nitrogen (TVB-N), ATP breakdown compounds, and colony-forming units were determined to predict the degradation of freshness. The image spectra obtained from visible and SW-NIR spectroscopy were related to the storage time of the samples. A PLS-DA model was developed independently for packaged or unpackaged samples using the second derivative of the spectra. Mean R2 prediction obtained for cooked ham was 0.915 and 0.949 for Turkey ham. The technique developed could be applied to monitoring the freshness of commercial packed cooked ham and turkey ham as a non-destructive technique. Further studies will be needed to check the spectra obtained from samples of different commercial brands in order to evaluate more precisely the efficiency of the method.

The current practice of having children begin school within a twelve month cohort is unfair but can be ameliorated by incorporating a dual-entry system. Such a reform effort will dramatically reduce failure, improve student achievement at all levels, while reducing expenditures.

It has been suggested that the Japanese sea cucumber, Apostichopus japonicus, has three color types (red, green, and black), although the qualitative difference between the color types, particularly between the green and black types, is unclear because of continuous color variation among color types. This study elucidated the color variation between green and black types using image processing (RGB, red-green-blue system) and multivariate analysis to demonstrate whether or not the black and green types can be quantitatively classified. Moreover, spatial variation of the RGB value among various local sites was clarified to estimate potential environmental factors that may affect the color variation. The series of analyses revealed that a quantitative boundary between green and black types could be provisionally established, and also that spatial variability in the intermediate (continuous) color trait between green and black types was significant. Potential environmental factors (depth and industrial activity index) were correlated with the color traits in both color types. These results suggest that the green and black types cannot be regarded as independent color traits and that the color variation between green and black types may be influenced by local environmental factors.