The Fast Integration of a Rotated Rectangle Applied to the Rotated Haar-like Features for Rotated Objects Detection

The Integral Image technique, used by Viola and Jones, is generally used to calculate the integral of a rectangular filter in an input picture. This filter is a rectilinear rectangle. We propose a method to integrate a rotated one by any angle of rotation inside an image based on the Bresenham algorithm of drawing a segment. We use some pixels 2013; called key points - that forms the four segments of a rotated rectangle, to calculate its Integral Image. Our method focuses on three essential tasks; the first is to determine the rule for drawing a segment (SDR), the second is to identify all the key points of the rectangle r, and the third is to calculate the integral image. The speed of this method depends on the size and angle of rotation of the rectangle. To demonstrate the efficiency of our idea, we applied it to the rotated Haar-like features that we proposed in a later work [12], which had as objectives the improvement of the Viola and Jones algorithm to detect the rotated faces in a given image. We performed tests on more widespread databases of images, which showed that the application of this technique to rotated Haar-Like features improves the performance of object detectors, in general, and faces in particular

Real-time embedded eye detection system

The detection of a person’s eyes is a basic task in applications as important as iris recognition in biometric identification or fatigue detection in driving assistance systems. Current commercial and research systems use software frameworks that require a dedicated computer, whose power consumption, size, and price are significantly large. This paper presents a hardware-based embedded solution for eye detection in real-time. From an algorithmic point-of-view, the popular Viola-Jones approach has been redesigned to enable highly parallel, single-pass image-processing implementation. Synthesized and implemented in an All-Programmable System-on-Chip (AP SoC), this proposal allows us to process more than 88 frames per second (fps), taking the classifier less than 2 ms per image. Experimental validation has been successfully addressed in an iris recognition system that works with walking subjects. In this case, the prototype module includes a CMOS digital imaging sensor providing 16 Mpixels images, and it outputs a stream of detected eyes as 640 × 480 images. Experiments for determining the accuracy of the proposed system in terms of eye detection are performed in the CASIA-Iris-distance V4 database. Significantly, they show that the accuracy in terms of eye detection is 100%.This work has been partially developed within the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds, and the ARMORI project (CEIATECH-10) funded by the University of Málaga. Portions of the research in this paper use the CASIA-Iris V4 collected by the Chinese Academy of Sciences - Institute of Automation (CASIA)

Speeding up boosted cascade of object detection using commodity graphics hardware

Master'sMASTER OF SCIENC

GPU acceleration of object classification algorithms using NVIDIA CUDA

The field of computer vision has become an important part of today\u27s society, supporting crucial applications in the medical, manufacturing, military intelligence and surveillance domains. Many computer vision tasks can be divided into fundamental steps: image acquisition, pre-processing, feature extraction, detection or segmentation, and high-level processing. This work focuses on classification and object detection, specifically k-Nearest Neighbors, Support Vector Machine classification, and Viola & Jones object detection. Object detection and classification algorithms are computationally intensive, which makes it difficult to perform classification tasks in real-time. This thesis aims in overcoming the processing limitations of the above classification algorithms by offloading computation to the graphics processing unit (GPU) using NVIDIA\u27s Compute Unified Device Architecture (CUDA). The primary focus of this work is the implementation of the Viola and Jones object detector in CUDA. A multi-GPU implementation provides a speedup ranging from 1x to 6.5x over optimized OpenCV code for image sizes of 300 x 300 pixels up to 2900 x 1600 pixels while having comparable detection results. The second part of this thesis is the implementation of a multi-GPU multi-class SVM classifier. The classifier had the same accuracy as an identical implementation using LIBSVM with a speedup ranging from 89x to 263x on the tested datasets. The final part of this thesis was the extension of a previous CUDA k-Nearest Neighbor implementation by exploiting additional levels of parallelism. These extensions provided a speedup of 1.24x and 2.35x over the previous CUDA implementation. As an end result of this work, a library of these three CUDA classifiers has been compiled for use by future researchers

Robotic Assembly Using 3D and 2D Computer Vision

The content of this thesis concerns the development and evaluation of a robotic cell used for automated assembly. The automated assembly is made possible by a combination of an eye-inhand 2D camera and a stationary 3D camera used to automatically detect objects. Computer vision, kinematics and programming is the main topics of the thesis. Possible approaches to object detection has been investigated and evaluated in terms of performance. The kinematic relation between the cameras in the robotic cell and robotic manipulator movements has been described. A functioning solution has been implemented in the robotic cell at the Department of Production and Quality Engineering laboratory. Theory with significant importance to the developed solution is presented. The methods used to achieve each part of the solution is anchored in theory and presented with the decisions and guidelines made throughout the project work in order to achieve the final solution. Each part of the system is presented with associated results. The combination of these results yields a solution which proves that the methods developed to achieve automated assembly works as intended. Limitations, challenges and future possibilities and improvements for the solution is then discussed. The results from the experiments presented in this thesis demonstrates the performance of the developed system. The system fulfills the specifications defined in the problem description and is functioning as intended considering the instrumentation used