Ames vision group research overview

A major goal of the reseach group is to develop mathematical and computational models of early human vision. These models are valuable in the prediction of human performance, in the design of visual coding schemes and displays, and in robotic vision. To date researchers have models of retinal sampling, spatial processing in visual cortex, contrast sensitivity, and motion processing. Based on their models of early human vision, researchers developed several schemes for efficient coding and compression of monochrome and color images. These are pyramid schemes that decompose the image into features that vary in location, size, orientation, and phase. To determine the perceptual fidelity of these codes, researchers developed novel human testing methods that have received considerable attention in the research community. Researchers constructed models of human visual motion processing based on physiological and psychophysical data, and have tested these models through simulation and human experiments. They also explored the application of these biological algorithms to applications in automated guidance of rotorcraft and autonomous landing of spacecraft. Researchers developed networks for inhomogeneous image sampling, for pyramid coding of images, for automatic geometrical correction of disordered samples, and for removal of motion artifacts from unstable cameras

Illumination-invariant vegetation detection for a vision sensor-based agricultural applications

In this paper, we propose a novel method, illumination-invariant vegetation detection (IVD), to improve many aspects of agriculture for vision-based autonomous machines or robots. The proposed method derives new color feature functions from simultaneously modeling the spectral properties of the color camera and scene illumination. An experiment in which an image sample dataset was acquired under nature illumination, including various intensities, weather conditions, shadows and reflections, was performed. The results show that the proposed method (IVD) yields the highest performance with the lowest error and standard deviation and is superior to six typical methods. Our method has multiple strengths, including computational simplicity and uniformly high-accuracy image segmentation

A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed

Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation

Image segmentation is a fundamental process in many image, video, and computer vision applications. Object extraction and object recognition are typical applications that use segmentation as a low level image processing. Most of the existing color image segmentation approaches, define a region based on color similarity. This assumption often makes it difficult for many algorithms to separate the objects of interest which consist of highlights, shadows and shading which causes inhomogeneous colors of the objects’ surface. Bayesian classification and decision making are based on probability theory and choosing the most probable or the lowest risk. A useful property of the statistical classifier like Bayesian is that, it is optimal in the sense that it minimizes the expected mis classification rate. However when the number of features increased, Bayesian classifier is quite expensive both in terms of computational time and memory. This thesis proposes a Bayesian color segmentation method which is robust and simple for real time color segmentation even in presence of environmental light effect. In this study a decision boundary equation, which is acquired from class conditional probability density function (PDF) of colors, based on Bayes decision theory has been used for desired color segmentation. The estimation of unknown PDF is a common problem and in this study Gaussian kernel function which is most widely used nonparametric density estimation method has been used for PDF calculation. Comparisons were made between the proposed method to the k-nearest neighbor (KNN) and support vector machine (SVM), methods for image segmentation. Experimental results show that the proposed algorithm works better than other two methods in terms of classifier accuracy with result of more than 99 percent successful segmentation of desired color in varying illumination. In order to show the real time ability and robustness of proposed method for color segmentation, experimental results conducted on vision based mobile robot for navigation. First the robot was trained by some training sample of desired target color in environment. The decision boundary which acquired in the teaching phase has been used for real time color segmentation as the robot move in the environment. Spatial information of desired color in segmented image has been used for calculating the robot heading angle which is used by mobile robot controller for navigation. However, all of the existing color image segmentation approaches are strongly application dependent. This study shows that proposed algorithm successfully cope with the varying illumination which causes uneven colors of the objects’ surface. The experimental results show the proposed algorithm is simple and robust, for real time application on vision based mobile robot for navigation, in spite of presence of other shapes and colors in the environmen

Real-time automatic multilevel color video thresholding using a novel class-variance criterion

[[abstract]]Color image segmentation is a crucial preliminary task in robotic vision systems. This paper presents a novel automatic multilevel color thresholding algorithm to address this task efficiently. The proposed algorithm consists of a learning process and a multi-threshold searching process. The learning process learns the color distribution of an input video sequence in HSV color space, and the multi-threshold searching process automatically determines the optimal multiple thresholds to segment all colors-of-interest in the video based on a novel class-variance criterion. For the learning process, a simple and efficient color-distribution learning algorithm operating with a color-pixel extraction method is proposed to learn a color distribution model of all colors-of-interest in the video images, which simplifies the search for optimal thresholds for the colors-of-interest through a conventional multilevel thresholding method. For the multi-threshold searching process, a nonparametric multilevel color thresholding algorithm with an extended within-class variance criterion is proposed to automatically find the optimal upper bound and lower bound threshold values of each color channel. Experimental results validate the performance and computational efficiency of the proposed method by comparing with three existing methods, both visually and quantitatively.[[booktype]]紙

Development of a Vision-Based Mobile Robot Navigation System for Golf Balls Detection and Location

A significant challenge in the design of an autonomous mobile robot is the reliable detection of targets, obstacles and targets tracking. Many types of sensor are used for that purposes such as infrared, sonar, vision sensor and laser. Monocular vision is one of the methods used due to simplicity and computational cost compared to stereo vision. Based on current trends the autonomous mobile robot development, vision sensor is used as different functions such as target recognition, obstacles avoidance, and navigation. To fulfill such demands the mobile robot should be able to estimate the distance of the detected targets and their angles from its current location. From the extracted information, the motions of the mobile robot can be done efficiently for targets retrieval task. This thesis addresses issue on golf balls localization. The sensor used for localization is a single color webcam. The experiment involves stationary golf balls localization at indoor and outdoor scene. The objective is to localize golf balls at various locations to be retrieved by the mobile robot. The distance towards the golf balls are estimated based on their diameter. This is based on the perspective view concept where the golf ball sizes are inversely proportional to their distance from webcam. Golf balls detection is done using color segmentation in RGB (red, green and blue) color space. A vector, a, that represents mean value of the target sample is calculated. Then the mean and standard deviation of each color component is calculated. The threshold value lies in the range μ ± σ which represents a square bounding box in RGB color space with a center at a. Every pixel in the test image is tested whether it lies within the bounding box which contributes to target pixel. The technique for segmentation can avoid high computation time for color image processing. The simple features such as diameter, x-y ratio and area are used as its inputs to the k-nearest neighbors (K-NN) classifier. The software is developed in Visual Basic 6 with a laptop computer acts as a controller and for handling image acquisition and processing. The localization process takes less than one second to be completed. The technique has been tested at indoor and outdoor environment. The efficiency of the estimation is more than 90 percents with a condition that the targets are less than 50 percents occluded

Animal coloration patterns: linking spatial vision to quantitative analysis

Animal coloration patterns, from zebra stripes to bird egg speckles, are remarkably varied. With research on the perception, function, and evolution of animal patterns growing rapidly, we require a convenient framework for quantifying their diversity, particularly in the contexts of camouflage, mimicry, mate choice, and individual recognition. Ideally, patterns should be defined by their locations in a low-dimensional pattern space that represents their appearance to their natural receivers, much as color is represented by color spaces. This synthesis explores the extent to which animal patterns, like colors, can be described by a few perceptual dimensions in a pattern space. We begin by reviewing biological spatial vision, focusing on early stages during which neurons act as spatial filters or detect simple features such as edges. We show how two methods from computational vision—spatial filtering and feature detection—offer qualitatively distinct measures of animal coloration patterns. Spatial filters provide a measure of the image statistics, captured by the spatial frequency power spectrum. Image statistics give a robust but incomplete representation of the appearance of patterns, whereas feature detectors are essential for sensing and recognizing physical objects, such as distinctive markings and animal bodies. Finally, we discuss how pattern space analyses can lead to new insights into signal design and macroevolution of animal phenotypes. Overall, pattern spaces open up new possibilities for exploring how receiver vision may shape the evolution of animal pattern signals

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201