The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD ...

Principal Manifolds for Data Visualization and Dimension Reduction

Vision-based change detection for inspection of tunnel liners

Technological advances are being made to assist humans in performing ordinary tasks in everyday settings. A key issue is the interaction with objects of varying size, shape, and degree of mobility. Autonomous assistive robots must be provided with the ability to process visual data in real time so that they can react adequately for quickly adapting to changes in the environment. Reliable object detection and recognition is usually a necessary early step to achieve this goal. In spite of significant research achievements, this issue still remains a challenge when real-life scenarios are considered. In this article, we present a vision system for assistive robots that is able to detect and recognize objects from a visual input in ordinary environments in real time. The system computes color, motion, and shape cues, combining them in a probabilistic manner to accurately achieve object detection and recognition, taking some inspiration from vision science. In addition, with the purpose of processing the input visual data in real time, a graphical processing unit (GPU) has been employed. The presented approach has been implemented and evaluated on a humanoid robot torso located at realistic scenarios. For further experimental validation, a public image repository for object recognition has been used, allowing a quantitative comparison with respect to other state-of-the-art techniques when realworld scenes are considered. Finally, a temporal analysis of the performance is provided with respect to image resolution and the number of target objects in the scene.

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Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

We investigate the use of multiple intrinsic geometric attributes, including angles, geodesic distances, and curvatures, for 3D face recognition, where each face is represented by a triangle mesh, preprocessed to possess a uniform connectivity. As invariance to facial expressions holds the key to improving recognition performance, we propose to train for the component-wise weights to be applied to each individual attribute, as well as the weights used to combine the attributes, in order to adapt to expression variations. Using the eigenface approach based on the training results and a nearest neighbor classifier, we report recognition results on the expression-rich GavabDB face database and the well-known Notre Dame FRGC 3D database. We also perform a cross validation between the two databases.

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Adapting Geometric Attributes for Expression-Invariant 3D Face Recognition

Improved deep learning framework for fish segmentation in underwater videos

This paper describes an efficient FPGA based hardware design for different image processing, enhancement, and filtering algorithms. FPGAs are often used as implementation platforms for real-time image processing applications because their structure is able to exploit spatial and temporal parallelism. The approach used is a windowing operator technique to traverse the pixels of an image, and apply the filters to them. As image sizes bit depth grow larger, software becomes less useful and real-time hardware systems are needed to take their place. The results are obtained for image size of 585×450, but the approach discussed can be used for images of any size, as long as the FPGA memory will hold it. The implementation was created with the Xilinx Spartan-6 FPGA on a Nexys3 board in mind.

Implementing image processing algorithms in FPGA hardware

In this paper an object recognition system using template matching is implemented Since objects are represented by either an external or internal descriptors, a combination of using signature, principal component analysis and color is used The system efficacy is measured by applying it to recognize an image of a chessboard with a set of objects (pieces) The output of the system includes the pieces names, locations and color The signature feature is used to distinguish the pieces types based on their external shape but when it falls short, the principal components analysis is used instead The object color is also obtained The matching between features is carried out based on Euclidean distance metric The proposed system is implemented, trained, and tested using Matlab based on a set of collected samples representing chessboard images The simulation results show the effectiveness of the proposed method in recognizing the pieces locations, types, and color

Object recognition system using template matching based onsignature and principal component analysis

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service PaaS to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface MPI is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

In this paper an Arabic Optical Character Recog- nition system is implemented. The system takes a scanned image of an Arabic text as an input and generates an editable text out of it. The system starts by segmenting the document which is presented as an image into lines, then each line is also segmented into separate words, after that each word is further segmented to sub-words. Each word or sub-word is segmented into separate characters, and then a features extraction process is applied on each character to calculate its features vector. The feature vector is then compared with templates of feature vectors for each of the Arabic alphabet with their variations. The minimum distance classifier is used in the classification stage. A recognition rate of 93.5% is attained. To improve the accuracy of the system, a lookup dictionary is employed to correct some of the misclassified characters. This resulted in improving the accuracy to 96.1%. The results achieved are promising regardless that Arabic Optical Character Recognition is considered many times harder to handle than its counterparts in other languages like English due to the continuity between the letters in the same word. Index Terms—Arabic OCR, Arabic characters, Segmenta- tion, Recognition, Image processing

/pdf/automated-system-for-arabic-optical-character-recognition-1j7fsvi2ox.pdf

Automated System for Arabic Optical Character Recognition with Lookup Dictionary

This paper presents efficient low-power compact hardware designs for common image processing functions including the median filter, smoothing filter, motion blurring, emboss filter, sharpening, Sobel, Roberts, and Canny edge detection. The designs were described in Verilog HDL. Xilinx ISE design suite was used for code simulation, synthesis, implementation, and chip programming. The designs were all evaluated in terms of speed, area number of LUTs and registers, and power consumption. Post placement and routing Post-PAR results show that they need very small area and consume very little power while achieving good frame per second rate even for HDTV high resolution frames. This makes them suitable for real-time applications with stringent area and power budgets.

Inad Aljarrah

Papers

Implementing image processing algorithms in FPGA hardware

Object recognition system using template matching based onsignature and principal component analysis

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

Automated System for Arabic Optical Character Recognition with Lookup Dictionary

Efficient Low-Power Compact Hardware Units for Real-Time Image Processing