Multi-Scale Edge Detection Algorithms and Their Information-Theoretic Analysis in the Context of Visual Communication

The unrealistic assumption that noise can be modeled as independent, additive and uniform can lead to problems when edge detection methods are applied to low signal-to-noise ratio (SNR) images. The main reason for this is because the filter scale and the threshold for the gradient are difficult to determine at a regional or local scale when the noise estimate is on a global scale. Therefore, in this dissertation, we attempt to solve these problems by using more than one filter to detect the edges and discarding the global thresholding method in the edge discrimination. The proposed multi-scale edge detection algorithms utilize the multi-scale description to detect and localize edges. Furthermore, instead of using the single default global threshold, a local dynamic threshold is introduced to discriminate between edges and non-edges. The proposed algorithms also perform connectivity analysis on edge maps to ensure that small, disconnected edges are removed. Experiments where the methods are applied to a sequence of images of the same scene with different SNRs show the methods to be robust to noise. Additionally, a new noise reduction algorithm based on the multi-scale edge analysis is proposed. In general, an edge—high frequency information in an image—would be filtered or suppressed after image smoothing. With the help of multi-scale edge detection algorithms, the overall edge structure of the original image could be preserved when only the isolated edge information that represents noise gets filtered out. Experimental results show that this method is robust to high levels of noise, correctly preserving the edges. We also propose a new method for evaluating the performance of edge detection algorithms. It is based on information-theoretic analysis of the edge detection algorithms in the context of an end-to-end visual communication channel. We use the information between the scene and the output of the edge-detection algorithm, ala Shannon, to evaluate the performance. An edge detection algorithm is considered to have high performance only if the information rate from the scene to the edge approaches the maximum possible. Therefore, this information-theoretic analysis becomes a new method to allow comparison between different edge detection operators for a given end-to-end image processing system

Design of a hybrid measure for image similarity: A statistical, algebraic, and information-theoretic approach

Image similarity or distortion assessment is fundamental to a wide range of applications throughout the field of image processing and computer vision. Many image similarity measures have been proposed to treat specific types of image distortions. Most of these measures are based on statistical approaches, such as the classic SSIM. In this paper, we present a different approach by interpolating the information theory with the statistic, because the information theory has a high capability to predict the relationship among image intensity values. Our unique hybrid approach incorporates information theory (Shannon entropy) with a statistic (SSIM), as well as a distinctive structural feature provided by edge detection (Canny). Correlative and algebraic structures have also been utilized. This approach combines the best features of Shannon entropy and a joint histogram of the two images under test, and SSIM with edge detection as a structural feature. The proposed method (ISSM) has been tested versus SSIM and FSIM under Gaussian noise, where good results have been obtained even under a wide range of PSNR. Simulation results using the IVC and TID2008 image databases show that the proposed approach outperforms the SSIM and FSIM approaches in similarity and recognition of the image

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Automated Intruder Detection from Image Sequences using Minimum Volume Sets

We propose a new algorithm based on machine learning techniques for automatic intruder detection in surveillance networks.  The algorithm is theoretically founded on the concept of minimum volume sets.  Through application to image sequences from two different scenarios and comparison with some existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates

Foveated object recognition by corner search

textHere we describe a gray scale object recognition system based on foveated corner finding, the computation of sequential fixation points, and elements of Lowe’s SIFT transform. The system achieves rotational, transformational, and limited scale invariant object recognition that produces recognition decisions using data extracted from sequential fixation points. It is broken into two logical steps. The first is to develop principles of foveated visual search and automated fixation selection to accomplish corner search. The result is a new algorithm for finding corners which is also a corner-based algorithm for aiming computed foveated visual fixations. In the algorithm, long saccades move the fovea to previously unexplored areas of the image, while short saccades improve the accuracy of putative corner locations. The system is tested on two natural scenes. As an interesting comparison study we compare fixations generated by the algorithm with those of subjects viewing the same images, whose eye movements are being recorded by an eyetracker. The comparison of fixation patterns is made using an information-theoretic measure. Results show that the algorithm is a good locator of corners, but does not correlate particularly well with human visual fixations. The second step is to use the corners located, which meet certain goodness criteria, as keypoints in a modified version of the SIFT algorithm. Two scales are implemented. This implementation creates a database of SIFT features of known objects. To recognize an unknown object, a corner is located and a feature vector created. The feature vector is compared with those in the database of known objects. The process is continued for each corner in the unknown object until enough information has been accumulated to reach a decision. The system was tested on 78 gray scale objects, hand tools and airplanes, and shown to perform well.Electrical and Computer Engineerin

An Approach to the Detection of Retinoblastoma based on Apriori Algorithm

Retinoblastoma is a rare kind of cancer, typically designated as leukocoria (white-eye pupillary reflex) that rapidly develops from the immature cells of a retina, the light-detecting tissue of the eye. It is the most common malignant cancer of the eye in young children. Early detection of leukocoria can improve the overall treatment duration.There is intensification in interest for setting up medical system that can monitor a large number of people for sight threatening diseases, likely Retinoblastoma and Diabetic Retinopathy.Developed an image processing application for the discovery of retinoblastoma by exploiting graph theory based apriori algorithm as a novel approach and different image processing techniques.The application will review the image with different phases and identifies region of interest of the threatened area in the retina.The software is implemented using MATLAB and developed a graphical user interface for smooth proceedings during identification stages of the disease