AER Neuro-Inspired interface to Anthropomorphic Robotic Hand

Address-Event-Representation (AER) is a communication protocol for transferring asynchronous events between VLSI chips, originally developed for neuro-inspired processing systems (for example, image processing). Such systems may consist of a complicated hierarchical structure with many chips that transmit data among them in real time, while performing some processing (for example, convolutions). The information transmitted is a sequence of spikes coded using high speed digital buses. These multi-layer and multi-chip AER systems perform actually not only image processing, but also audio processing, filtering, learning, locomotion, etc. This paper present an AER interface for controlling an anthropomorphic robotic hand with a neuro-inspired system.Unión Europea IST-2001-34124 (CAVIAR)Ministerio de Ciencia y Tecnología TIC-2003-08164-C03-02Ministerio de Ciencia y Tecnología TIC2000-0406-P4- 0

Iris Recognition: The Consequences of Image Compression

Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected

Image Captioning using Deep Neural Architectures

Automatically creating the description of an image using any natural languages sentence like English is a very challenging task. It requires expertise of both image processing as well as natural language processing. This paper discuss about different available models for image captioning task. We have also discussed about how the advancement in the task of object recognition and machine translation has greatly improved the performance of image captioning model in recent years. In addition to that we have discussed how this model can be implemented. In the end, we have also evaluated the performance of model using standard evaluation matrices.Comment: Pre-print version of paper accepted at 2017 International Conference on Innovations in information Embedded and Communication Systems (ICIIECS

A Benchmark for Image Retrieval using Distributed Systems over the Internet: BIRDS-I

The performance of CBIR algorithms is usually measured on an isolated workstation. In a real-world environment the algorithms would only constitute a minor component among the many interacting components. The Internet dramati-cally changes many of the usual assumptions about measuring CBIR performance. Any CBIR benchmark should be designed from a networked systems standpoint. These benchmarks typically introduce communication overhead because the real systems they model are distributed applications. We present our implementation of a client/server benchmark called BIRDS-I to measure image retrieval performance over the Internet. It has been designed with the trend toward the use of small personalized wireless systems in mind. Web-based CBIR implies the use of heteroge-neous image sets, imposing certain constraints on how the images are organized and the type of performance metrics applicable. BIRDS-I only requires controlled human intervention for the compilation of the image collection and none for the generation of ground truth in the measurement of retrieval accuracy. Benchmark image collections need to be evolved incrementally toward the storage of millions of images and that scaleup can only be achieved through the use of computer-aided compilation. Finally, our scoring metric introduces a tightly optimized image-ranking window.Comment: 24 pages, To appear in the Proc. SPIE Internet Imaging Conference 200