SUMMARYCurrently available techniques for performing quantitative immunohistochemistry (Q-IHC) rely upon pixel-counting algorithms and therefore cannot provide information as to the absolute amount of chromogen present. We describe a novel algorithm for true Q-IHC based on calculating the cumulative signal strength, or energy, of the digital file representing any portion of an image. This algorithm involves subtracting the energy of the digital file encoding the control image (i.e., not exposed to antibody) from that of the experimental image (i.e., antibody-treated). In this manner, the absolute amount of antibody-specific chromogen per pixel can be determined for any cellular region or structure. Q uantitative immunohistochemistry (Q-IHC) in the predigital era depended on the observations of multiple investigators using an arbitrary scale to grade the extent and intensity of chromogen present (Shi et al. 1991(Shi et al. ,1993. This approach was inherently limited by observer subjectivity and bias, by inter-and intraobserver variation, and generated data of limited range (i.e., amount is usually quantified on a 0-4 scale). With the advent of digital photomicroscopy, however, these weaknesses could in theory be eliminated and true quantification achieved.Early attempts at Q-IHC involved converting analog images into a digital format and then transforming the 256 separate shades of red, green and blue that are obtained when working in 24-bit RGB color to singlechannel grayscale (Mosedale et al. 1996). The area of interest is defined and the mean gray level of the selected pixels determined. Later attempts at Q-IHC employed color thresholding using commercial software (i.e., Adobe Photoshop) (Fermin and Degraw 1995), followed by counting the total number of pixels of appropriate value (Lehr et al. 1997(Lehr et al. ,1999Ruifrok 1997). For example, in DAB-based immunohistochemistry all the pixels containing "brown" within a prespecified spectral range are identified in Photoshop using the Magic Wand tool. The total numbers of pixels identified are then counted using the histogram function (Lehr et al. 1999). Although these algorithms represent significant improvements over traditional methods for evaluating analog images, these approaches still do not allow true Q-IHC to be performed.Determining the absolute amount of chromogen present necessitates calculating the cumulative signal strength of the image being evaluated. This can be done only by calculating signal energy, E, which is defined in the mathematical (Jain 1989) and not the physical sense. Here we provide an algorithm for true Q-IHC that relies on calculating the energy of images captured in Photoshop using a high-resolution digital camera and then processing the image's unmodified and full digital file using the powerful enabler language Matlab. To demonstrate this algorithm, we studied the gastrin-releasing peptide receptor (GRPR) aberrantly expressed by human colon cancers. Because we have previously shown that human colon cancers variably express this ...

Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature.

Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams available from a range of devices including motion sensors, video cameras, haptic devices, etc. Developing view-invariant activity recognition algorithms based on this high dimensional cue is an extremely challenging task. This paper presents efficient activity recognition algorithms using novel view-invariant representation of trajectories. Towards this end, we derive two Affine-invariant representations for motion trajectories based on curvature scale space (CSS) and centroid distance function (CDF). The properties of these schemes facilitate the design of efficient recognition algorithms based on hidden Markov models (HMMs). In the CSS-based representation, maxima of curvature zero crossings at increasing levels of smoothness are extracted to mark the location and extent of concavities in the curvature. The sequences of these CSS maxima are then modeled by continuous density (HMMs). For the case of CDF, we first segment the trajectory into subtrajectories using CDF-based representation. These subtrajectories are then represented by their Principal Component Analysis (PCA) coefficients. The sequences of these PCA coefficients from subtrajectories are then modeled by continuous density hidden Markov models (HMMs). Different classes of object motions are modeled by one Continuous HMM per class where state PDFs are represented by GMMs. Experiments using a database of around 1750 complex trajectories (obtained from UCI-KDD data archives) subdivided into five different classes are reported.

Abstract-This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem of trajectory representation when only partial trajectory information is available due to occlusion. It is achieved by a hypothesis testing-based method applied to curvature data computed from trajectories. The subtrajectories are then represented by their principal component analysis (PCA) coefficients for optimally compact representation. Different techniques are integrated to index and retrieve subtrajectories, including PCA, spectral clustering, and string matching. We assume a query by example mechanism where an example trajectory is presented to the system and the search system returns a ranked list of most similar items in the dataset. Experiments based on datasets obtained from University of California at Irvine's KDD archives and Columbia University's DVMM group demonstrate the superiority of our proposed PCA-based approaches in terms of indexing and retrieval times and precision recall ratios, when compared to other techniques in the literature.

Abstract-We propose a fast object tracking algorithm that predicts the object contour using motion vector information. The segmentation step common in region-based tracking methods is avoided, except for the initialization of the object. Tracking is achieved by predicting the object boundary using block motion vectors followed by updating the contour using occlusions/disocclusion detection. An adaptive block-based approach has been used for estimating motion between frames. An efficient modulation scheme is used to control the gap between frames used for motion estimation. The algorithm for detecting disocclusion proceeds in two steps. First, uncovered regions are estimated from the displaced frame difference. These uncovered regions are classified into actual disocclusions and false alarms by observing the motion characteristics of uncovered regions. Occlusion and disocclusion are considered as dual events and this relationship is explained in detail. The algorithm for detecting occlusion is developed by modifying the disocclusion detection algorithm in accordance with the duality principle. The overall tracking algorithm is computationally superior to existing region-based methods for object tracking. The immediate applications of the proposed tracking algorithm are video compression using MPEG-4 and content retrieval based on standards like H.264. Preliminary simulation results demonstrate the performance of the proposed algorithm.