Region-Based Feature Interpretation for Recognizing 3D Models in 2D Images

In model-based vision, there are a huge number of possible ways to match model features to image features. In addition to model shape constraints, there are important match-independent constraints that can efficiently reduce the search without the combinatorics of matching. I demonstrate two specific modules in the context of a complete recognition system, Reggie. The first is a region-based grouping mechanism to find groups of image features that are likely to come from a single object. The second is an interpretive matching scheme to make explicit hypotheses about occlusion and instabilities in the image features.

Feature Point Detection and Curve Approximation for Early Processing of Freehand Sketches

Freehand sketching is both a natural and crucial part of design, yet is unsupported by current design automation software. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer to produce a design environment that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in the sketch into the intended geometric objects using feature point detection and approximation. We demonstrate how multiple sources of information can be combined for feature detection in strokes and apply this technique using two approaches to signal processing, one using simple average based thresholding and a second using scale space.

Feature Selection for Face Detection

We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.

On the difficulty of feature-based attentional modulations in visual object recognition: A modeling study.

Numerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural level, attention increases firing rates of neurons in visual cortex whose preferred stimulus is currently attended to. However, it is not yet known how these two phenomena are linked, i.e., how the visual system could be "tuned" in a task-dependent fashion to improve task performance. To answer this question, we performed simulations with the HMAX model of object recognition in cortex [45]. We modulated firing rates of model neurons in accordance with experimental results about effects of feature-based attention on single neurons and measured changes in the model's performance in a variety of object recognition tasks. It turned out that recognition performance could only be improved under very limited circumstances and that attentional influences on the process of object recognition per se tend to display a lack of specificity or raise false alarm rates. These observations lead us to postulate a new role for the observed attention-related neural response modulations.

Oblique Angle Deposition of Germanium Film on Silicon Substrate

The effect of flux angle, substrate temperature and deposition rate on obliquely deposited germanium (Ge) films has been investigated. By carrying out deposition with the vapor flux inclined at 87° to the substrate normal at substrate temperatures of 250°C or 300°C, it may be possible to obtain isolated Ge nanowires. The Ge nanowires are crystalline as shown by Raman Spectroscopy.

Poly-Si₁₋xGex Film Growth for Ni Germanosilicided Metal Gate

Scaling down of the CMOS technology requires thinner gate dielectric to maintain high performance. However, due to the depletion of poly-Si gate, it is difficult to reduce the gate thickness further especially for sub-65 nm CMOS generation. Fully silicidation metal gate (FUSI) is one of the most promising solutions. Furthermore, FUSI metal gate reduces gate-line sheet resistance, prevents boron penetration to channels, and has good process compatibility with high-k gate dielectric. Poly-SiGe gate technology is another solution because of its enhancement of boron activation and compatibility with the conventional CMOS process. Combination of these two technologies for the formation of fully germanosilicided metal gate makes the approach very attractive. In this paper, the deposition of undoped Poly-Si₁₋xGex (0 < x < 30% ) films onto SiO₂ in a low pressure chemical vapor deposition (LPCVD) system is described. Detailed growth conditions and the characterization of the grown films are presented.

Plasma polymerization of C[subscript 4]F[subscript 8] thin film on high aspect ratio silicon molds

High aspect ratio polymeric micro-patterns are ubiquitous in many fields ranging from sensors, actuators, optics, fluidics and medical. Second generation PDMS molds are replicated against first generation silicon molds created by deep reactive ion etching. In order to ensure successful demolding, the silicon molds are coated with a thin layer of C[subscript 4]F[subscript 8] plasma polymer to reduce the adhesion force. Peel force and demolding status are used to determine if delamination is successful. Response surface method is employed to provide insights on how changes in coil power, passivating time and gas flow conditions affect plasma polymerization of C[subscript 4]F[subscript 8].