Sparse representation based classification (SRC) has gained great success in image recognition. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may help improve the separability and margin between nearby data points, we propose Euler SRC for image classification, which is essentially the SRC with Euler sparse representation. To be specific, it first maps the images into the complex space by Euler representation, which has a negligible effect for outliers and illumination, and then performs complex SRC with Euler representation. The major advantage of our method is that Euler representation is explicit with no increase of the image space dimensionality, thereby enabling this technique to be easily deployed in real applications. To solve Euler SRC, we present an efficient algorithm, which is fast and has good convergence. Extensive experimental results illustrate that Euler SRC outperforms traditional SRC and achieves better performance for image classification.
One of today's more popular artificially intelligent (AI) androids comes from the TV series "MARVEL's Agents of S.H.I.E.L.D." Those of you who followed the latest season's story -- no spoilers here! One of the most interesting things about this fictional AI character is that it can read people's emotions. Thanks to researchers from the University of Cambridge, this AI ability might soon make the jump from sci-fi to reality. The first step in creating such a system is training an algorithm on simpler facial expressions and just one specific emotion or feeling. To that end, the Cambridge team focused on using a machine learning algorithm to figure out if a sheep is in pain, and this week, they presented their research at the IEEE International Conference on Automatic Face and Gesture Recognition in Washington, D.C.
Building on earlier work which teaches computers to recognise emotions and expressions in human faces, the system is able to detect the distinct parts of a sheep's face and compare it with a standardised measurement tool developed by veterinarians for diagnosing pain. Their results will be presented today (1 June) at the 12th IEEE International Conference on Automatic Face and Gesture Recognition in Washington, DC. Severe pain in sheep is associated with conditions such as foot rot, an extremely painful and contagious condition which causes the foot to rot away; or mastitis, an inflammation of the udder in ewes caused by injury or bacterial infection. Both of these conditions are common in large flocks, and early detection will lead to faster treatment and pain relief. Reliable and efficient pain assessment would also help with early diagnosis.
We propose a trajectory planning and control theory for continuous movements such as connected cursive handwriting and continuous natural speech. Its hardware is based on our previously proposed forward-inverse-relaxation neural network (Wada & Kawato, 1993). Computationally, its optimization principle is the minimum torquechange criterion.Regarding the representation level, hard constraints satisfied by a trajectory are represented as a set of via-points extracted from a handwritten character. Accordingly, we propose a via-point estimation algorithm that estimates via-points by repeating the trajectory formation of a character and the via-point extraction from the character. In experiments, good quantitative agreement is found between human handwriting data and the trajectories generated by the theory. Finally, we propose a recognition schema based on the movement generation. We show a result in which the recognition schema is applied to the handwritten character recognition and can be extended to the phoneme timing estimation of natural speech. 1 INTRODUCTION In reaching movements, trajectory formation is an ill-posed problem because the hand can move along an infinite number of possible trajectories from the starting to the target point.
Facebook could one day build facial gesture controls for its app thanks to the acquisition of a Carnegie Mellon University spinoff company called FacioMetrics. The startup made an app called Intraface that could detect seven different emotions in people's faces, but it's been removed from the app stores. The acquisition aligns with a surprising nugget of information Facebook slipped into a 32-bullet point briefing sent to TechCrunch this month. "Future applications of deep learning platform on mobile: Gesture-based controls, recognize facial expressions and perform related actions" It's not hard to imagine Facebook one day employing FacioMetrics' tech and its own AI to let you add a Like or one of its Wow/Haha/Angry/Sad emoji reactions by showing that emotion with your face. "How people share and communicate is changing and things like masks and other effects allow people to express themselves in fun and creative ways.