Statistical properties of thresholded images

We present an efficient algorithm suitable for VLSI implementation for extracting the line segments from the input grey scale image. We first apply an edge detection algorithm to the input image producing an edge map and also recording for each pixel an orientation and confidence level. To allow a pipelined implementation we scan this map in a raster fashion. The next step uses the orientation and confidence level data to interpolate missed edge pixels. Then each line segment is traced in parallel, with a prioritisation scheme dealing with segment intersection and applying colinearity restrictions to avoid merging two segments of different slopes. Since the raster scan oversegments lines that form a small positive angle with the horizontal axis, so the scan is repeated from right to left and the two results are merged. We describe a video-rate architecture for hardware implementation of our algorithm.

In this paper the problem of image feature extraction is considered with emphasis on developing methods which are resilient in the presence of data contamination. The issue of robustness of estimation procedures has received considerable attention in the statistics community [1–3] but its results are only recently being applied to specific image analysis tasks [4–7]. In this paper we show how the design of robust methods applies to image description tasks posed within a statistical hypothesis testing and parameter estimation framework. The methodology is illustrated by applying it to finding robust, optimal estimation kernels for line detection and edge detection. We then discuss the relationship of these optimal solutions to both the well established Hough Transform technique and the standard estimation kernels developed in the statistics literature. The application of standard robust kernels to image analysis tasks is illustrated by two examples which involve circular arc detection in gray-level imagery and planar surface segmentation in depth data. Robust methods are found to be effective general tools for generating 2D and 3D image descriptions.

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it. In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.

Image segmentation is a fundamental task in image analysis which is responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours. The goal of medical image segmentation is to partition a medical image in to separate regions, usually anatomic structures that are meaningful for a specific task. In many medical applications, such as diagnosis, surgery planning, and radiation treatment planning determining of the volume and position of an anatomic structure is required and plays a critical role in the treatment outcome.

Colorization of grayscale images is a hot topic in computer vision. Previous research mainly focuses on producing a color image to recover the original one in a supervised learning fashion. However, since many colors share the same gray value, an input grayscale image could be diversely colorized while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test on 80 humans further indicates our generated color schemes are highly convincible.

Colorization is a process of adding colors to a black and white image. There exists vast amounts of illustrations and photographs that lack color information such as modern medical imaging to antique photography. Visual appeal and expressiveness of these black and white images can be improved by adding colors. Image colorization is a challenging topic of current researches in Computer Visions. Significant user interaction is required in the form of placing numerous color scribbles, looking at related images etc for coloring the image in traditional or manual coloring of image. This is difficult because of the complex shades of colors present in the image. Automating the image colorization methods has various benefits in different field such as medical, art, entertainment etc. Applying machine learning techniques to this process reduces the human efforts substantially. Image colorization is the process of taking an input grayscale image and then producing an output colorized image. In this proposed chapter a technique of colorization of grayscale image using deep learning is discussed.

The paper presents a novel three-dimensional network and its application to pattern analysis. This is a multistage architecture which investigates partial correlations between structural image components. Initially the image is partitioned to be processed in parallel channels. In each channel, the structural components are transformed and subsequently separated depending on their informational activity, to be mixed with the components from other channels for further processing. An output result is represented as a pattern vector, whose components are computed one at a time to allow the quickest possible response. The paper presents an algorithm applied to facial images decomposition. The input gray-scale image is transformed so that each pixel contains information about the spatial structure of its neighborhood. A three-level representation of gray-scale image is used in order for each pixel to contain the maximum amount of structural information. The most correlated information is extracted first, making the algorithm tolerant to minor structural changes.

Recently, the development of confidentiality and authenticity ensured by image encryption has been one of the key advancements in the field of secured wireless communication. The proposed work focuses on providing confusion, diffusion and permutation inherently in the system. The input grayscale image is shuffled by employing the Henon algorithm and subsequently separated into 8 bit planes. With the aid of a secret key of 256 bits, chaotic sequences are generated for each bit plane. Later, the logistic map is adapted on the chaotic sequences to obtain the scrambled image. Furthermore, this image is shuffled with Zaslavskii and Hilbert Space Filling Curve algorithms, which establishes the confusion stage. To incorporate the diffusion stage, a self-invertible matrix is generated by a latin square image cipher and a secret key. This matrix and the scrambled image underwent the Hill cipher to build the encrypted image followed by a row-column transformation to ensure multifold security. The proposed compound activities of encryption are successfully implemented on the laboratory virtual instrumentation engineering workbench 2013 platform. Noticeably, the established processes of image encryption are tested in the universal software radio peripheral environment and transceived via an additive white Gaussian noise (AWGN) channel. More specifically, the influence of natural and unnatural (cropping attack) noise on the characteristics of the encrypted image while sharing through AWGN channel has been investigated. Security analysis is performed by computing the unified average changing intensity, number of pixels change rate, correlation value, large key space to defy brute force attack, strong key sensitivity and uniform gray value distribution on encryption.

Visual information, in the form of images and video, comes from the interaction of light with objects. Illumination is a fundamental element of visual information. Detecting and interpreting illumination effects is part of our everyday life visual experience. Shading for instance allows us to perceive the three-dimensional nature of objects. Shadows are particularly salient cues for inferring depth information. However, we do not make any conscious or unconscious effort to avoid them as if they were an obstacle when we walk around. Moreover, when humans are asked to describe a picture, they generally omit the presence of illumination effects, such as shadows, shading, and highlights, to give a list of objects and their relative position in the scene. Processing visual information in a way that is close to what the human visual system does, thus being aware of illumination effects, represents a challenging task for computer vision systems. Illumination phenomena interfere in fact with fundamental tasks in image analysis and interpretation applications, such as object extraction and description. On the other hand, illumination conditions are an important element to be considered when creating new and richer visual content that combines objects from different sources, both natural and synthetic. When taken into account, illumination effects can play an important role in achieving realism. Among illumination effects, shadows are often integral part of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In this thesis, the problem of extracting shadows from digital images is discussed. A new analysis method for the segmentation of cast shadows in still and moving images without the need of human supervision is proposed. The problem of separating moving cast shadows from moving objects in image sequences is particularly relevant for an always wider range of applications, ranging from video analysis to video coding, and from video manipulation to interactive environments. Therefore, particular attention has been dedicated to the segmentation of shadows in video. The validity of the proposed approach is however also demonstrated through its application to the detection of cast shadows in still color images. Shadows are a difficult phenomenon to model. Their appearance changes with changes in the appearance of the surface they are cast upon. It is therefore important to exploit multiple constraints derived from the analysis of the spectral, geometric and temporal properties of shadows to develop effective techniques for their extraction. The proposed method combines an analysis of color information and of photometric invariant features to a spatio-temporal verification process. With regards to the use of color information for shadow analysis, a complete picture of the existing solutions is provided, which points out the fundamental assumptions, the adopted color models and the link with research problems such as computational color constancy and color invariance. The proposed spatial verification does not make any assumption about scene geometry nor about object shape. The temporal analysis is based on a novel shadow tracking technique. On the basis of the tracking results, a temporal reliability estimation of shadows is proposed which allows to discard shadows which do not present time coherence. The proposed approach is general and can be applied to a wide class of applications and input data. The proposed cast shadow segmentation method has been evaluated on a number of different video data representing indoor and outdoor real-world environments. The obtained results have confirmed the validity of the approach, in particular its ability to deal with different types of content and its robustness to different physically important independent variables, and have demonstrated the improvement with respect to the state of the art. Examples of application of the proposed shadow segmentation tool to the enhancement of video object segmentation, tracking and description operations, and to video composition, have demonstrated the advantages of a shadow-aware video processing.

Medical images can be described by their power and phase spectra. Therefore, it is of interest to know how these components influence human observers in detection tasks. Whereas the power spectrum appears to correctly describe the useful statistical properties of computer generated noise images (like white noise, filtered white noise or lumpy backgrounds), this might not be the case for patient structured images. The present study investigates the role of stationarity, power and phase spectra of two types of medical images (mammography and angiography). We consider different categories of images that all have the same mean, and power spectrum. Two-alternative forced-choice experiments are performed on patient structured images, random phase, filtered white noise, and clustered lumpy background. This latter has the property to contain visible structures similar to the ones observed on real mammograms, and (unlike real patient structure) to be stationary by construction. It is shown that model observers can take non-stationarity into account of real images in two different ways. The safest and easiest way consists of applying the model template directly on the images. The other way consists of correcting the performance computed from global quantities with a factor that takes into account local statistical values in the area of interest. Finally, patient structured backgrounds are not fully described by their power spectrum and we show that human observers are able to use some information contained in the phase spectrum.

The GRAFIX I system was developed in the late 1960's as a fast flexible system for processing and analyzing filmed images, particularly of material which is essentially binary (black and white), such as printed text, line drawings, certain biomedical images, fingerprints, etc. It incorporates a large scale general purpose time shared computer to provide the facilities for the efficient development of algorithms necessary to perform various image processing and analysis tasks. In addition it contains a fast, high resolution flying-spot film scanner and a powerful and rather general slave processor (the binary image processor, or BIP) which provide data collection and manipulation facilities adequate to perform image processing and analysis tasks at commercially practical speeds. So far it has been successfully employed in a commercial environment to the reading of printed multifont text in complex page formats, and to the reading of Cyrillic, Greek and even handprinted text. At present we are considering future applications beyond the area of optical character recognition, particularly the analysis of engineering drawings, as well as the automatic analysis and classification of fingerprints, the analysis of biomedical images such as chromosomes, the analysis of x-ray images, and the analysis of satellite imagery data, among others.

The work is devoted to developing the main results in solving the fundamental problem of formalization and systematization of methods and forms of information representation in image analysis, recognition, and understanding tasks. This problem arises in connection with development of the descriptive approach to image analysis and understanding. The main result is the concept of image spatial formalization, including a set of image states and a set of states of image transformation schemes. We consider the construction of algorithmic schemes generating phase trajectories for solving problems of image analysis and recognition. We present axioms defining the properties and structure of the image formalization space. The introduced system of concepts constitute the basis for standardizing methods for synthesizing image models oriented to image analysis and understanding tasks.

Segmentation is always a difficult task in image analysis. In this paper, we propose a solution to computing distance transforms in images with fuzzy object borders. The difference from a standard distance transform is in the initialisation, which takes the fuzziness of the border into account. As an example of its usefulness, the new fuzzy border distance transform is used in skeletonization.

Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization.