Developing a real-time translator from neural signals to text: An articulatory phonetics approach

New developments in brain-computer interfaces (BCI) harness machine learning to decode spoken language from electrocorticographic (ECoG) and local field potential (LFP) signals. Orienting to signals associated with motor movements that produce articulatory features improves phoneme detection quality: individual phonemes share features, but possess a unique feature set; classification by feature set allows for a finer distinction between neural signals. Data indicates vowels are more detectable, consonants have greater detection accuracy, place of articulation informs precision, and manner of articulation affects recall. Findings have implications for the multisensory integration of speech and the role of motor imagery in phonemic neural representations

Reconstruction of medical images by perspective shape-from-shading

Shape-from-Shading (SfS) is a fundamental problem in Computer Vision; it is based upon the image irradiance equation. Recently, the authors proposed to solve the image irradiance equation under the assumption of perspective projection rather than the common orthographic one. The solution was a modification of the Fast Marching method of Kimmel and Sethian. This paper presents an application of this novel perspective algorithm to reconstruction of medical images. We focus on gastrointestinal endoscopy and compare the two versions of the Fast Marching method (orthographic vs. perspective). The examples and comparison show that, unlike orthographic SfS, perspective SfS is robust and can be utilized for real-life applications. 1

Photometric stereo under perspective projection

Photometric stereo is a fundamental approach in Computer Vision. At its core lies a set of image irradiance equations each taken with a different illumination. The vast majority of studies in this field have assumed orthography as the projection model. This paper re-examines the basic set of equations of photometric stereo, under an assumption of perspective projection. We show that the resulting system is linear (as is the case under the orthographic model; Nevertheless, the unknowns are different in the perspective case). We then suggest a simple reconstruction algorithm based on the perspective formulae, and compare it to its orthographic counterpart on synthetic as well as real images. This algorithm obtained lower error rates than the orthographic one in all of the error measures. These findings strengthen the hypothesis that a more realistic set of assumptions, the perspective one, improves reconstruction significantly. 1

for Geometric Computing, and the Moscona fund.

Camouflage is frequently used by animals and humans (usually for military purposes) in order to conceal objects from visual surveillance or inspection. Most camouflage methods are based on superpositioning multiple edges on the object that is supposed to be hidden, such that its familiar contours and texture are masked. In this work, we present an operator, (Darg), that is applied directly to the intensity image in order to detect 3D smooth convex (or equivalently: concave) objects. The operator maximally responds to a local intensity configuration that corresponds to curved 3D objects, and thus, is used to detect curved objects on a relatively flat background, regardless of image edges, contours and texture. In that regard, we show that a typical camouflage found in some animal species, seems to be a ”counter measure ” taken against detection that might be based on our method. Detection by Darg is shown to be very robust, from both theoretic considerations and practical examples of real-life images. As a part of the camouflage breaking demonstration, Darg, which is non-edge-based, is compared with a representative edge-based operator. Better performance is maintained by Darg for both animal and military camouflage breaking. Key Words: convexity detection, regions of interest, camouflage breaking, counter shading. CONVEXITY-BASED VISUAL CAMOUFLAGE BREAKING

Y.: A model for visual camouflage breaking

Abstract. Some animals use counter-shading in order to prevent their detection by predators. Counter-shading means that the albedo of the animal is such that its image has a flat intensity function rather than a convex intensity function. This implies that there might exist predators who can detect 3D objects based on the convexity of the intensity function. In this paper, we suggest a mathematical model which describes a possible explanation of this detection ability. We demonstrate the effectiveness of convexity based camouflage breaking using an operator (“Darg”) for detection of 3D convex or concave graylevels. Its high robustness and the biological motivation make Darg particularly suitable for camouflage breaking. As will be demonstrated, the operator is able to break very strong camouflage, which might delude even human viewers. Being non-edgebased, the performance of the operator is juxtaposed with that of a representative edge-based operator in the task of camouflage breaking. Better performance is achieved by Darg for both animal and military camouflage breaking.