Low Complexity Image Recognition Algorithms for Handheld devices

Content Based Image Retrieval (CBIR) has gained a lot of interest over the last two decades. The need to search and retrieve images from databases, based on information (“features”) extracted from the image itself, is becoming increasingly important. CBIR can be useful for handheld image recognition devices in which the image to be recognized is acquired with a camera, and thus there is no additional metadata associated to it. However, most CBIR systems require large computations, preventing their use in handheld devices. In this PhD work, we have developed low-complexity algorithms for content based image retrieval in handheld devices for camera acquired images. Two novel algorithms, ‘Color Density Circular Crop’ (CDCC) and ‘DCT-Phase Match’ (DCTPM), to perform image retrieval along with a two-stage image retrieval algorithm that combines CDCC and DCTPM, to achieve the low complexity required in handheld devices are presented. The image recognition algorithms run on a handheld device over a large database with fast retrieval time besides having high accuracy, precision and robustness to environment variations. Three algorithms for Rotation, Scale, and Translation (RST) compensation for images were also developed in this PhD work to be used in conjunction with the two-stage image retrieval algorithm. The developed algorithms are implemented, using a commercial fixed-point Digital Signal Processor (DSP), into a device, called ‘PictoBar’, in the domain of Alternative and Augmentative Communication (AAC). The PictoBar is intended to be used in the field of electronic aid for disabled people, in areas like speech rehabilitation therapy, education etc. The PictoBar is able to recognize pictograms and pictures contained in a database. Once an image is found in the database, a corresponding associated speech message is played. A methodology for optimal implementation and systematic testing of the developed image retrieval algorithms on a fixed point DSP is also established as part of this PhD work

Low complexity image recognition algorithm for handheld applications

We propose a low complexity image recognition algorithm based on Content Based Image Retrieval (CBIR) suitable for handheld applications. The target application is an Alternative and Augmentative Communication (AAC) device used in speech rehabilitation and education. The device recognizes images (pictograms and pictures) and plays a sound message associated with the recognized image. Experimental validation of the proposed algorithm using MATLAB and its DSP implementation is presented

Rotation, Scale and Translation invariant image retrieval method based on Circular Segmentation and Color Density

We propose a fast and efficient method for Content Based Image Retrieval (CBIR) which uses color densities within concentric circular zones of the image, encompassing edge-pixels. This method is invariant to Rotation, Scale and Translation (RST). Small-sized feature vectors are used to store and effectively characterize the color content of the image. Consequently the memory and time required for data querying are reduced. This computationally inexpensive method is suited for portable applications. We briefly present an example of application in a handheld pictogram recognition device, used for rehabilitation and education, in which the proposed method is used as pre-selection stage of a heavier method for reducing complexity while keeping recognition accuracy

Implementation of an image recognition algorithm on the DM6446 DaVinci Processor

We are developing an Alternative and Augmentative Communication (AAC) portable device called PictoBar which is used in speech rehabilitation therapy. PictoBar recognizes barcodes and images, such as pictograms and pictures. Then it plays a sound message associated with the recognized barcode or image. This paper describes the development of the image recognition algorithm and its implementation using Codec Engine framework on a DM6446 DaVinci processor

Portable augmentative and alternative communication device with pictogram recognition functionality

The field of electronic aid for disabled people has been growing constantly with many new innovations being added every year. The need for electronic aid in alternative and augmentative communication (ACC) is becoming increasingly important. Devices which describe extra ways of helping people who find it hard to communicate by speech or writing are being introduced into the market every year. A next generation device for alternative and augmentative communication which shall be used in rehabilitation therapies for speech-impaired people is being developed at EPFL-IMT. This device includes smart image processing capabilities to offer new possibilities to train users and to let them express in a more intuitive and interactive manner. The user acquires an image (pictogram, picture or barcode). The device recognizes the image and plays its corresponding pre-recorded audio sequence. This device is intended to be used by language re-education professionals and specialized educators in the treatment of people affected by pathologies such as aphasia, autism, trisomy or mental handicaps