Christine Podilchuk

Image recovery by convex projections using a least-squares constraint

We introduce a new convex constraint for image recovery using the method of projection onto convex sets. The set of least-squares solutions to the image-recovery problem is shown to form a convex set. The projection operator onto this set is presented. The resulting least-squares projection method is shown to provide improved performance over conventional projection techniques.

A New Face Recognition Algorithm using Bijective Mappings

A new face recognition algorithm is proposed which is robust to variations in pose, expression and illumination. The framework is similar to the ubiquitous block matching algorithm used for motion estimation in video compression but has been adapted to compensate for illumination differences. One of the key differentiators of this approach is that unlike traditional face recognition algorithms, the image data representing the face or features extracted from the facial data is not used for classification. Instead, the mapping between the probe and gallery images given by the block matching algorithm is used to classify the faces for recognition. Once the mappings are found for each gallery image, the degree of bijectivity that each mapping produces is used to derive the similarity scores for recognition.

Robust face recognition using advanced correlation filters with bijective-mapping preprocessing

In this paper we explore performing robust face verification using Advanced Correlation Filters on Bijective-Mapping preprocessed face images. We show that using the proposed Bijective-Mapping preprocessing method we can increase verification performance (at 0.1% FAR) significantly in our experiments using the Face Recognition Grand Challenge (FRGC) database collected by the University of Notre Dame consisting of 152 subjects. This recognition experiment is challenging as the results reported on these experiments utilize only a single gallery image from each subject during the training phase and the probe images are captured in different time-lapsed sessions which vary mostly in pose and facial expression.

Speckle reduction of medical ultrasound using Compressive Re-Sampling and instantaneous SNR

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or microcalcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. Traditional speckle reduction techniques attempt to remove speckle noise while preserving edges and other important features but there is always a tradeoff between removing the speckle noise and blurring tissue structure and details. We introduce a novel speckle reduction and contrast enhancement method for ultrasound imaging that is motivated by the fundamental ideas behind compressive sampling. We also introduce a way to estimate instantaneous SNR in order to identify the areas that are mostly signal from the areas that are mostly noise in order to preserve the signal while suppressing the noise. We have shown improvements in SNR on the order of 12dB in the lab and improved visualization of clinical data.

Speckle reduction using stepped-frequency continuous wave ultrasound

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. Ultrasound imaging is particularly useful in breast cancer detection and diagnosis for women with dense breast tissue where traditional mammography may fail to detect suspicious areas. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or micro-calcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. In conventional pulsed ultrasound imaging systems, de-noising techniques are used to minimize the effect of speckle noise. However, research shows that there is a tradeoff between the effectiveness of speckle reduction techniques and image resolution. We introduce stepped-frequency continuous wave ultrasound imaging which provides a framework where speckle reduction techniques are particularly effective, resulting in higher quality images with an improved SNR and significantly lower speckle noise while maintaining the spatial resolution of the original scan so that small lesions of interest are visible to the radiologist.

Resolution Enhancement Of Reconstructed Images By Using Image Restoration Techniques

The Modified Row Action Projection (MRAP) algorithm is used to increase the spatial resolution of images reconstructed via the Filter Back Projection (FBP) algorithm.

Face recognition using a pictorial-edit distance

A new face recognition algorithm is introduced that is based on finding an optimal mapping between two images and measuring the properties of the mapping to determine the similarity between the two images. This new method is shown to be extremely robust to variations in pose, camera position and lighting for the problem of face recognition especially in an uncontrolled environment such as video surveillance. The technique does not depend on locating or training on facial features and can be applied in general to any object recognition problem. We present recognition results on current databases available to the research community and will demonstrate a real-time version of the software for access control and surveillance applications at the conference.

Preprocessing for improved computer aided detection in medical ultrasound

Recently, a new speckle noise reduction and contrast enhancement technique has been introduced that is motivated by the research in compressive sampling or sensing. Compressive sampling is based on the principle that a sparse signal such as ultrasound can be fully recovered when sampled below the Nyquist rate. This allows for a new noise reduction technique that preserves the high frequency and fine details while reducing the effects of speckle noise. This method improves the overall perceptual quality of the image for visualization and diagnosis by the radiologist. This paper examines how the improvement in SNR makes the method suitable as a preprocessor to improve a computer aided detection (CAD) system for breast cancer detection. Classical performance metrics such as false positive rates, false negative rates and receiver operator curves will be used to show the benefits of this approach. Initial experiments look promising for microcalcification detection, where the new method yields a false negative rate of 20 percent at a false positive rate of 0.5 percent while the traditional speckle reduction techniques yield a false negative rate of 60 percent at a false positive rate of 0.5 percent.

Face recognition for uncontrolled environments

A new face recognition algorithm has been proposed which is robust to variations in pose, expression, illumination and occlusions such as sunglasses. The algorithm is motivated by the Edit Distance used to determine the similarity between strings of one dimensional data such as DNA and text. The key to this approach is how to extend the concept of an Edit Distance on one-dimensional data to two-dimensional image data. The algorithm is based on mapping one image into another and using the characteristics of the mapping to determine a two-dimensional Pictorial-Edit Distance or P-Edit Distance. We show how the properties of the mapping are similar to insertion, deletion and substitution errors defined in an Edit Distance. This algorithm is particularly well suited for face recognition in uncontrolled environments such as stand-off and other surveillance applications. We will describe an entire system designed for face recognition at a distance including face detection, pose estimation, multi-sample fusion of video frames and identification. Here we describe how the algorithm is used for face recognition at a distance, present some initial results and describe future research directions.(

Projection Techniques For Image Restoration

In this paper we compare the use of three different projection techniques for image recovery. The three methods include modified versions of the row-action and block-action projection techniques of Kaczmarz as well as a new iterative projection method which projects onto the set of least squares solutions. The performance characteristics of the three techniques are demonstrated using computer simulations.