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In this paper, a novel method for detecting steadystate visual evoked potentials (SSVEP) using multiple channel electroencephalogram (EEG) data is presented. Accurate asynchronous detection, high speed and high information transfer rate can be achieved after a short calibration session. Spatial filtering based on the Canonical Corelation Analysis method proposed in [1] is used for identifying optimal...
The quality of measuring the image is a complicated and difficult process since humans opinion score is affected by physical and psychological factors. Several techniques are proposed for measuring the image quality but any of this techniques is considered to be ideal for measuring the image quality. Image quality assessment plays a significant role in the field of image processing. Image quality...
In this paper, we are mostly interested in investigating how the study and discovery of the human visual cortex could be utilised to improve the computational models for visual recognition by computer vision. Many of the brain perceptual abilities in vision have corresponding algorithms exist in computer vision, and in this paper we discuss three such models. First we present a model that has the...
This paper introduces a novel method that effectively and efficiently encodes the spatial geometric information of bag of visual words (BoW) to boost the performance of large scale partial duplicate image discovery and clustering. The loose cyclic spatial verification (LCSV) technique projects the locations of BoWs onto the perimeter of a circle centred around their geometric centroid and encodes...
Fisher vectors (FV) aggregated from local invariant features (e.g., SIFT) is one of the state-of-the-art descriptors for visual search, due to high discriminability but small visual vocabulary. Nevertheless, a high-dimensional FV needs to be compressed into a compact descriptor for light storage and high matching eficiency. In this paper, we formulate the FV compression as a resource-constrained optimization...
This paper presents a novel image classification method based on integration of EEG and visual features. In the proposed method, we obtain classification results by separately using EEG and visual features. Furthermore, we merge the above classification results based on a kernelized version of Supervised learning from multiple experts and obtain the final classification result. In order to generate...
This paper presents a hybrid method for single-source localization in wireless sensor networks, fusing noisy range measurements with angular information extracted from video. Although recent works found in the literature explore hybrid schemes, these include several cumbersome assumptions. We develop and test, both numerically and experimentally, a hybrid localization algorithm which surpasses the...
Motivated by the recent progresses in the use of deep learning techniques for acoustic speech recognition, we present in this paper a visual deep bottleneck feature (DBNF) learning scheme using a stacked auto-encoder combined with other techniques. Experimental results show that our proposed deep feature learning scheme yields approximately 24% relative improvement for visual speech accuracy. To the...
Due to the ongoing biodiversity crisis, many species including great apes such as chimpanzees or gorillas are threatened and need to be protected. To overcome the catastrophic decline of biodiversity, biologists recently started to use remote cameras for wildlife monitoring. However, the manual analysis of the resulting image and video material is extremely tedious, time consuming, and highly cost...
Existing techniques usually adopt compact descriptors such as Fisher vector for mobile visual search, since compact descriptors are memory-efficient and suitable for fast transmission. In common Fisher vector methods, in order to make the size of image representations small enough for efficient transmission, only a small number of visual words are used. However, this choice usually sacrifices the...
Interactive mobile vision applications, such as Mobile Landmark Recognition (MLR), have recently attracted ever increasing research attention due to the exponential growth of mobile devices. However, the recognition accuracy retains as a bottleneck hesitating the proliferation of such applications. To address this challenge, in this paper we design a novel framework based on interactive image segmentation...
Traditional speech recognition systems use Gaussian mixture models to obtain the likelihoods of individual phonemes, which are then used as state emission probabilities in hidden Markov models representing the words. In hybrid systems, the Gaussian mixtures are replaced by more discriminant classifiers, leading to an improved performance. Most of the time the classifiers used in such systems are neural...
Cognitive robotics looks at human cognition as a source of inspiration for automatic perception capabilities that will allow robots to learn and reason out how to behave in response to complex goals. For instance, humans learn to recognize object categories ceaselessly over time. This ability to refine knowledge from the set of accumulated experiences facilitates the adaptation to new environments...
The use of Image data is growing tremendously in every field such as medical, engineering designs, fashion, interior designs, and education etc. For this growing need of image data, we also need to have an efficient and effective tool for its retrieval. This increasing need of content based image retrieval technique can be rising in a number of other different domains. In this paper the researcher...
The coronary cine-angiogram (CCA) is an invasive medical image modality which is used to determine the luminal obstructions or stenosis in the Coronary Arteries (CA). CCA based quantitative assessment of vascular morphology is a demanding area in medical diagnosis and segmentation of blood vessels in CCAs is one of the mandatory step in this endeavor. The accurate segmentation of CAs in Angiogram...
This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character...
A three-dimensional accelerometer was incorporated into a balance board to report real-time ankle attitudes during ankle sprain rehabilitative treatment. The design consisted of an embedded sensory module wirelessly linked to an Android mobile device with a graphic user interface to assess proprioceptive improvements. Preliminary results demonstrated an ability to report ankle attitude at a rate of...
Many studies have reported that older adults with glaucoma experience mobility issues due to gait difficulties. These include walking slowly and bumping into obstacles, which increase the risk of falls in glaucoma patients. In this paper, we design and develop a shoe-integrated sensing system as well as signal processing and machine learning algorithms to objectively quantify gait patterns in glaucoma...
Buildings require regular maintenance, and augmented reality (AR) could advantageously be used to facilitate the process. However, such AR systems would require accurate tracking to meet the needs of engineers, and work accurately in entire buildings. Popular tracking systems based on visual features cannot easily be applied in such situations, because of the limited number of visual features indoor,...
We aim to develop a brain-machine interface (BMI) system that estimates user's gaze or attention on an object to pick it up in the real world. In Experiment 1 and 2 we measured steady-state visual evoked potential (SSVEP) using luminance and/or contrast modulated flickers of photographic scenes presented on a head-mounted display (HMD). We applied multiclass SVM to estimate gaze locations for every...
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