A morphable model for the synthesis of 3D faces

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Digital processing of speech signals

Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

Activation Functions: Comparison of trends in Practice and Research for Deep Learning

"A picture is worth one thousand words". This proverb comes from Confucius a Chinese philosopher before about 2500 years ago. Now, the essence of these words is universally understood. A picture can be magical in its ability to quickly communicate a complex story or a set of ideas that can be recalled by the viewer later in time. Visual information plays an important role in our society, it will play an increasingly pervasive role in our lives, and there will be a growing need to have these sources processed further. The pictures or images are used in many application areas like architectural and engineering design, fashion, journalism, advertising, entertainment, etc. Thus it provides the necessary opportunity for us to use the abundance of images. However, the knowledge will be useless if one can't _nd it. In the face of the substantive and increasing apace images, how to search and to retrieve the images that we interested with facility is a fatal problem: it brings a necessity for image retrieval systems. As we know, visual features of the images provide a description of their content. Content-based image retrieval (CBIR), emerged as a promising mean for retrieving images and browsing large images databases. CBIR has been a topic of intensive research in recent years. It is the process of retrieving images from a collection based on automatically extracted features.

/pdf/a-survey-of-shape-feature-extraction-techniques-3qmkhziio7.pdf

A Survey of Shape Feature Extraction Techniques

Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.

An overview of character recognition focused on off-line handwriting

https://user.ceng.metu.edu.tr/~nafiz/papers/PRLET_2003.pdf

BAS: a perceptual shape descriptor based on the beam angle statistics

A new analytic scheme, which uses a sequence of image segmentation and recognition algorithms, is proposed for the off-line cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, stroke width and height, are estimated. Second, a segmentation method finds character segmentation paths by combining gray-scale and binary information. Third, a hidden Markov model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in the HMM training stage together with the estimation of the HMM model parameters. Finally, information from a lexicon and from the HMM ranks is combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by the segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments indicate higher recognition rates compared to the available methods reported in the literature.

https://user.ceng.metu.edu.tr/~nafiz/papers/PAMI_2002.pdf

Optical character recognition for cursive handwriting

In the present study, both single channel electroencephalography (EEG) complexity and two channel interhemispheric dependency measurements have newly been examined for classification of patients with obsessive–compulsive disorder (OCD) and controls by using support vector machine classifiers. Three embedding entropy measurements (approximate entropy, sample entropy, permutation entropy (PermEn)) are used to estimate single channel EEG complexity for 19-channel eyes closed cortical measurements. Mean coherence and mutual information are examined to measure the level of interhemispheric dependency in frequency and statistical domain, respectively for eight distinct electrode pairs placed on the scalp with respect to the international 10–20 electrode placement system. All methods are applied to short EEG segments of 2 s. The classification performance is measured 20 times with different 2-fold cross-validation data for both single channel complexity features (19 features) and interhemispheric dependency features (eight features). The highest classification accuracy of 85 ±5.2% is provided by PermEn at prefrontal regions of the brain. Even if the classification success do not provided by other methods as high as PermEn, the clear differences between patients and controls at prefrontal regions can also be obtained by using other methods except coherence. In conclusion, OCD, defined as illness of orbitofronto-striatal structures [Beucke et al., JAMA Psychiatry 70 (2013) 619–629; Cavedini et al., Psychiatry Res. 78 (1998) 21–28; Menzies et al., Neurosci. Biobehav. Rev. 32(3) (2008) 525–549], is caused by functional abnormalities in the pre-frontal regions. Particularly, patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations. Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders exhibited a decreased complexity (such as anorexia nervosa [Toth et al., Int. J. Psychophysiol. 51(3) (2004) 253–260] and panic disorder [Bob et al., Physiol. Res. 55 (2006) S113–S119]).

Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements.

Unmanned aerial vehicles (UAVs) domain has seen rapid developments in recent years. As the number of UAVs increases and as the missions involving UAVs vary, new research issues surface. An overview of the existing research areas in the UAV domain has been presented including the nature of the work categorised under different groups. These research areas are divided into two main streams: Technological and operational research areas. The research areas in technology are divided into onboard and ground technologies. The research areas in operations are divided into organization level, brigade level, user level, standards and certifications, regulations and legal, moral, and ethical issues. This overview is intended to serve as a starting point for fellow researchers new to the domain, to help researchers in positioning their research, identifying related research areas, and focusing on the right issues. Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 319-329, DOI: http://dx.doi.org/10.14429/dsj.65.8631

http://publications.drdo.gov.in/ojs/index.php/dsj/article/download/8631/4999

Nafiz Arica

Papers

An overview of character recognition focused on off-line handwriting

BAS: a perceptual shape descriptor based on the beam angle statistics

Optical character recognition for cursive handwriting

Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements.

Unmanned Aerial Vehicle Domain: Areas of Research