Hafiz Adnan Habib

Real time mono vision gesture based virtual keyboard system

This paper presents a novel mono-vision virtual keyboard design for consumers of mobile and portable computing devices such as PDAs, mobile phones etc. Fuzzy approaches to gesture recognition are developed to reveal the key pressed over the printed sheet keyboard by analyzing the hand and finger gesture captured in the video sequence. Real time system is developed by integrating SDIO camera with PDA in the application environment. Reliable results are experienced by the implementation of the proposed real time mono vision gestured virtual keyboard system

Infotainment devices control by eye gaze and gesture recognition fusion

This paper presents a novel concept for controlling the consumer devices such as MP3 player or other daily life appliances by fusion of eye gaze and gesture recognition methodologies. Such system is deployable for virtually controlling the consumer devices anywhere in the home and office. The usability of the system is also tested with patients at the intensive care units of the hospitals where the patients may not able to operate the consumer devices in a regular way. The proposed system consists of a video processing based embedded system, a CCD camera and situated display. Physical control options are displayed over the situated display. Selection / de-selection process of displayed control option over situated display is accomplished by analyzing the video sequence captured by the CCD camera. Eye gaze estimation and head gesture recognition algorithms analyze the video sequence and reveal the control command that has to be sent to the infotainment or other consumer devices.

A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words

Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval.

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs) with artificial neural networks (ANNs). Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.

A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

Real-time feet movement detection and tracking for controlling a Toy car

This paper proposes a new approach of controlling vehicles by detection of visual foot movement. It discusses the constraints imposed by the use of moving vehicle during real-time foot movement tracking. The visual feet tracking attributes ensure the fact that, unlike its counterpart in physical world, our driving does not involve effort of steering. The proposed technique comprises of two main parts; Software part and Hardware part. The Software part consists of the steps, namely, Initialization phase for initializing the software according to environmental conditions, Feature extraction phase to extract features, parameters calculation phase to calculate parameters in order to determine direction of motion. The hardware part consists of Toy cars RC alteration and Communication from personal computer using serial or parallel port. It works in real-time optimizing the problems caused by shadows. The output parameters are used for detection of any movement made by foot. So the input to the software part is the stream of images of foot and the output produced by the software part are the parameters specifying direction of motion. These parameters are input to the hardware part, which in this case is a toy car, and the hardware performs desired action.

Road Boundary Detection in Night Video Sequence: A Novel Technique for Autonomous Vehicles

This paper presents the enhancement of road boundaries in the test video recorded at night time. This is fact that road boundaries become of more importance in night time driving when loss of light makes every thing harder to see. Road boundary detection is a basic and vital research work area because it is a prerequisite for automatic pilot in the intelligent vehicles system project. The boundary detection is a process of analyzing images obtained by vehicle-mounted camera to obtain meaningful information. The research work initially focuses the problem of image enhancement and then for road boundary extraction. Though many different techniques were developed for the extraction of road boundary, the results were not up to the mark as with non uniform illumination a variety of shadows and reflections are created making drastic changes in the actual road scene. The proposed road boundary detection technique brings the preprocessed image stream into frequency domain for fast processing and the video signals are analyzed. In two dimension array of signals the low frequency signals embed in high frequencies are treated differently by bringing them to a very low amplitude signals or zero. The video signal array is then converted into the spatial domain again for further processing. This novel idea can be applied on both painted and unpainted roads, curved and straight roads and with different illumination conditions.

Priority Queue Based Reactive Buffer Management Policy for Delay Tolerant Network under City Based Environments

Delay Tolerant Network (DTN) multi-copy routing protocols are privileged to create and transmit multiple copies of each message that causes congestion and some messages are dropped. This process is known as reactive drop because messages were dropped re-actively to overcome buffer overflows. The existing reactive buffer management policies apply a single metric to drop source, relay and destine messages. Hereby, selection to drop a message is dubious because each message as source, relay or destine may have consumed dissimilar magnitude of network resources. Similarly, DTN has included time to live (ttl) parameter which defines lifetime of message. Hence, when ttl expires then message is automatically destroyed from relay nodes. However, time-to-live (ttl) is not applicable on messages reached at their destinations. Moreover, nodes keep replicating messages till ttl expires even-though large number of messages has already been dispersed. In this paper, we have proposed Priority Queue Based Reactive Buffer Management Policy (PQB-R) for DTN under City Based Environments. The PQB-R classifies buffered messages into source, relay and destine queues. Moreover, separate drop metric has been applied on individual queue. The experiment results prove that proposed PQB-R has reduced number of messages transmissions, message drop and increases delivery ratio.

Computer vision based detection and localization of potholes in asphalt pavement images

Asphalt pavement distresses have significant importance in roads and highways. This paper addresses the detection and localization of one of the key pavement distresses, the potholes using computer vision. Different kinds of pothole and non-pothole images from asphalt pavement are considered for experimentation. Considering the appearance-shape based nature of the potholes, Histograms of oriented gradients (HOG) features are computed for the input images. Features are trained and classified using Naïve Bayes classifier resulting in labeling of the input as pothole or non-pothole image. To locate the pothole in the detected pothole images, normalized graph cut segmentation scheme is employed. Proposed scheme is tested on a dataset having broad range of pavement images. Experimentation results showed 90 % accuracy for the detection of pothole images and high recall for the localization of pothole in the detected images.