Heuristic collision-free path planning for an autonomous platform

In this paper, a heuristic and learning, algorithmic scheme for collision-free navigation is presented. This scheme determines an optimum collision-free navigation path of an autonomous platform by using a trial and error process, past navigation knowledge and current information extracted from the generated surrounding environment.     

Segmentation of Colour Images with Highlights and Shadows sing Fuzzy-like Reasoning

In this paper, a fuzzy-like technique is presented that resolves several difficult issues related to image segmentation, such as highlights and shadows. Large, relatively continuous, areas within an image are usually easy to segment, and the pixels included within different segments are often determined by using derived edge information. However, in many cases, pixels which lie between segments or in high frequency areas of an image cannot be easily categorised as belonging to any particular segments. Typically, according to the dichromatic reflection model, these pixels may belong to the matte, highlight or shadow area of the closest segment; or, in association with neighbouring pixels, they make up a separate smaller segment. The dichromatic reflection model is applied here to merge highlight and shadow areas with matte areas in an image. By segmenting those pixels into proper regions, the proposed fuzzy-like reasoning approach provides a more human-like segmentation of images.     

A Reconfigurable Logic-Based Processor for the SCAN Image and Video Encryption Algorithm

This paper presents a detailed architecture and a reconfigurable logic based hardware design of the SCAN algorithm. This architecture can be used to encrypt high resolution images in real-time. Although the SCAN algorithm is a block cipher algorithm with arbitrarily large blocks, the present design is for 64×64 pixel blocks in order to provide real-time image encryption throughput. The architecture was initially targeted at the Xilinx XCV-1000 FPGA, for which design and performance results are presented in the paper.     

A 3-D visualization method for image-guided brain surgery

This paper deals with a 3D methodology for brain tumor image-guided surgery. The methodology is based on development of a visualization process that mimics the human surgeon behavior and decision-making. In particular, it originally constructs a 3D representation of a tumor by using the segmented version of the 2D MRI images. Then it develops an optimal path for the tumor extraction based on minimizing the surgical effort and penetration area. A cost function, incorporated in this process, minimizes the damage surrounding healthy tissues taking into consideration the constraints of a new snake-like surgical tool proposed here. The tumor extraction method presented in this paper is compared with the ordinary method used on brain surgery, which is based on a straight-line based surgical tool. Illustrative examples based on real simulations present the advantages of the 3D methodology proposed here.     

Segmentation of color images using multiscale clustering and graph theoretic region synthesis

A multiresolution color image segmentation approach is presented that incorporates the main principles of region-based segmentation and cluster-analysis approaches. The contribution of This work may be divided into two parts. In the first part, a multiscale dissimilarity measure is proposed that makes use of a feature transformation operation to measure the interregion relations with respect to their proximity to the main clusters of the image. As a part of this process, an original approach is also presented to generate a multiscale representation of the image information using nonparametric clustering. In the second part, a graph theoretic algorithm is proposed to synthesize regions and produce the final segmentation results. The latter algorithm emerged from a brief analysis of fuzzy similarity relations in the context of clustering algorithms. This analysis indicates that the segmentation methods in general may be formulated sufficiently and concisely by means of similarity relations theory. The proposed scheme produces satisfying results and its efficiency is indicated by comparing it with: 1) the single scale version of dissimilarity measure and 2) several earlier graph theoretic merging approaches proposed in the literature. Finally, the multiscale processing and region-synthesis properties validate our method for applications, such as object recognition, image retrieval, and emulation of human visual perception.     

Emulating human visual perception for measuring difference inimages using an SPN graph approach

This paper presents a new methodology for efficiently representing the content of images and comparing images by detecting and recording their visual differences. In particular, the methodology presented here is based on a stochastic Petri-net (SPN) graph approach able to extract and record local and global features from both images, compare them, and define the percentage of similarity. One of the features of the human visual perception is the detection of similarities between two images. The visual similarity is based on color, size, shape, and local and global topological changes of the image regions. Several methods dealing with image or object similarities have been proposed. The new feature of the method here is the partial emulation of the human observer's visual perception by recording differences extracted from different images. Results of the method described here are presented for a variety of images by using local and global noisy conditions.     

A knowledge-based expert system for automatic visual VLSI reverse-engineering: VLSI layout version

This paper presents a method of knowledge representation for very large scale integration (VLSI) chip design which provides the necessary information for abstraction from the physical design to gate-level logic through a high-level behavioral model. The representation scheme used by the ANTISTROFEAS system utilizes a hierarchical attributed graph structure which consists of incrementally abstracted design information for the VLSI system. This method of knowledge representation is well-suited to reverse-engineering of VLSI chips from the layer mask layout data, but is also applicable to applications at many levels of the design process including design rule checking, logic synthesis, design verification, and partitioning-compaction problems. The representation scheme is applicable to any VLSI technology, and is designed to take advantage of artificial intelligence. expert system techniques, by disassociating the representation and manipulation of the VLSI design data from the rules which govern its correctness and transformation for other usage.     

A survey of skin-color modeling and detection methods

Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well.     

Three-dimensional reconstruction of the digestive wall in capsule endoscopy videos using elastic video interpolation

Wireless capsule endoscopy is a revolutionary technology that allows physicians to examine the digestive tract of a human body in the minimum invasive way. Physicians can detect diseases such as blood-based abnormalities, polyps, ulcers, and Crohn's disease. Although this technology is really a marvel of our modern times, currently it suffers from two serious drawbacks: 1) frame rate is low (3 frames/s) and 2) no 3-D representation of the objects is captured from the camera of the capsule. In this paper we offer solutions (methodologies) that deal with each of the above issues improving the current technology without forcing hardware upgrades. These methodologies work synergistically to create smooth and visually friendly interpolated images from consecutive frames, while preserving the structure of the observed objects. They also extract and represent the texture of the surface of the digestive tract in 3-D. Thus the purpose of our methodology is not to reduce the time that the gastroenterologists need to spend to examine the video. On the contrary, the purpose is to enhance the video and therefore improve the viewing of the digestive tract leading to a more qualitative and efficient examination. The proposed work introduces 3-D capsule endoscopy textured results that have been welcomed by Digestive Specialists, Inc., Dayton, OH. Finally, illustrative results are given at the end of the paper.     

Data-image-video encryption

This paper describes the most representative algorithms and standards for the encryption of data, digital images and MPEG video. The general model a typical encryption/decryption system about the security principle is discussed. Data encryption mainly is the scrambling of the content of data, text, image, audio, and video and to make the data unreadable, invisible or incomprehensible during ciphertext transmission. The goal is to protect the content of the data against the attackers. The reverse of data encryption is data decryption, which recovers the original data. There are two types of encryption/decryption key: the public-key system and the private-key system. The most promising features are joint lossless compression, joint encryption and hiding based on SCAN language which analyses the unique properties of digital image and video and search for high security algorithms to reduce the overall computational cost.