PPT-HISTOGRAMS

Representing Data Why use a Histogram When there is a lot of data When data is Continuous a mass height volume time etc Presented in a Grouped Frequency Distribution Often in groups or classes that are UNEQUAL . Hadjidemetriou M D Grossberg and S K Nayar Computer Science Columbia University New York NY 10027 stathis mdog nayar cscolumbiaedu Abstract The histogram of image intensities is used extensively for the retrieval of images from visual databases An o g BinetCauchy kernels However such approaches are only applicable to time series data living in a Euclidean space eg joint trajectories extracted from motion capture data or feature point trajectories extracted from video Much of the success of rec lcsmitedu Piotr Indyk MIT indyktheorylcsmitedu Sudipto Guha University of Pennsylvania sudiptocisupennedu Nick Koudas ATT Research koudasresearchattcom ABSTRACT Histograms are a concise and flexible way to construct sum mary structures for large data Scott 1983 1985b considered the problem of choosing among the collection of multivariate frequency polygons each with the same smoothing parameter but di64256ering bin origins Rather than choosing the smoothest such curve or surface he proposed aver histograms of DCT coefficients as the macroscopic quantity Details of this approach can be found in [8]. Because OutGuess preserves the first order statistics (histogram), we cannot use the same appr Navneet. . Dalal. and Bill . Triggs. CVPR 2005. Another Descriptor. Overview. 1. Compute gradients in the region to be described. 2. Put them in bins according to orientation. 3. Group the cells into large blocks. Percentiles and Percentile Ranks and their Graphical Representations. Note: we’ll be skipping book sections:. 2.4 (apparent and real limits) . 2.8, 2.9 (percentile and percentile ranks for grouped data). Lack of knowledge vs. variability.. What type of safety measures do we take?. Design, manufacturing, operations & post-mortems. Living with uncertainties vs. changing them. How do we represent random variables?. M. . Barni. , M. Fontani, B. . Tondi. , G. Di . Domenico. Dept. of Information Engineering, University of Siena (IT). Outline. MultiMedia. Forensics & Counter-Forensics. Universal counter-forensics. Jeong. , . Dongseok. There are two techniques used for Video Fingerprinting : CPF(Color Patches Features) and Gradient Histograms. What is the main idea of these techniques?. What methods are used for similar image searching?. Lack of knowledge vs. variability.. What type of measures do we take to reduce uncertainty?. Design, manufacturing, operations & post-mortems. Living with uncertainties vs. changing them. How do we represent random variables?. multimodal is always interesting. qualitative distinction!. Regimes or modes (literal meaning). Buoy downwelling radiation along Pacific cold tongue as TIWs go by in the ocean. Figure 3.16. – Width vs. top height of each EO for 16 June 2006 – 31 May 2008. Distribution is weighted by total number of pixels (volume) per EO to emphasize larger EOs. Bin size is 10 pixels (~ km) by 240 m. Width is the horizontal span of the EO.. Module #7 – Statistics. Topics. Statistics. Histograms & Bar Plots. Scaled Histograms / Probability . Textbook Reading Assignments. 7.1-7.2. Practice Problems. Chapter 7 Problems: . 7.1, 7.2. 1) . Brave New Data. We are no longer limited to charts which only work for categorical data.. We have three more charts at our disposal.. Even though I do not think the book stresses this enough, frequency tables and relative frequency tables are still useful for quantitative data..