On histograms and spatiograms - introduction of the mapogram

Nilsson, Mikael; Bartunek, Josef Ström; Nordberg, Jörgen; Claesson, Ingvar

doi:10.1109/icip.2008.4711919

Cited by 10 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) However a powerful representation nevertheless histogram based methods looses spatial information. In contrast to its histogram counterpart, spatiogram based representation is used to include the much needed spatiotemporal information [41], [42], [43] to track under cluttered background but at the cost of increased memory requirements because the 2D map is retained. 3) Combining MS with the Kalman Filter (KF) has relaxed the full occlusion constraint however when the object is lost, the number of iterations to converge increases tremendously and the tracker becomes very slow.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

Integration of Mean-Shift and Particle Filter: A Survey

Khattak¹,

Raja

Anjum

et al. 2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

Object tracking has become the cornerstone of many computer vision applications. Numerous object tracking methods have surfaced in the research community which are intended for high level applications such as automatic data analysis for activity recognition. Most of the methods are either too constrained in the context of the given application or they are costly in terms of computations to meet the real-time requirements. For example, Mean-Shift (MS) has rose to prominence due to its ease of implementation and robustness to various deformations however it fails to track objects with small sizes, fast motion and full occlusion. On the other hand, Particle Filter (PF) is known for its efficiency, accuracy and robustness to small sizes, fast object motion and full occlusion however it is heavily influenced by the number of particles, besides the sample degeneracy and impoverishment problems. Decoupling the disadvantages of both the methods gave birth to a new era of modern trackers known as hybrid systems that are more efficient, accurate and robust to the aforementioned constraints.A few survey papers on object tracking has been published in the scientific circles during the last decade however we feel that this popular integration of MS and PF is still unregistered.

show abstract

Section: State-of-the-art Methodsmentioning

confidence: 99%

Integration of Mean-Shift and Particle Filter: A Survey

Khattak¹,

Raja

Anjum

et al. 2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

show abstract

“…Histograms have proved themselves to be a powerful representation for image data in a region. Discarding all spatial information, they are the foundation of classic techniques such as histogram equalization and image indexing [12]. The color histogram of an image is relatively invariant with translation and rotation about the viewing axis, and varies only slowly with the angle of view [13].…”

Section: Overview Of Features Extracted: 21 Histogrammentioning

confidence: 99%

"Mammographic Image Enhancement, Classification and Retrieval using Color, Statistical and Spectral Analysis"

Bikesh¹

2011

IJCA

View full text Add to dashboard Cite

One of the major causes of cancer death among middle aged women in developed countries is breast cancer. Mammography is one method used by radiologists for detection and interpretation of cancer in breast images. Over the past few years Content-based Image Retrieval (CBIR) approaches has received significant attention for medical images analysis. In this paper content based retrieval techniques were tested for tissue classification and analysis of breast images. The proposed method employs image enhancement, analysis and classification of mammograms using histogram, statistical, wavelet coefficients and spectral features. The implementation of proposed method was carried out using MATLAB software and hence can work on simple personal computer. Analysis was carried out on 56 images collected from open source mini-MIAS database. Euclidean distance was used to compare the features of query image with stored images in database. Results show that the suggested features can be used for both classification and retrieval of mammographic images. The retrieval efficiency was obtained to be 85.7%.

show abstract

“…histogram, spatiogram and mapogram) "ogram" approaches. Properties of "ogram" approaches can be summarized as follows: (1) the histogram discards all spatial information contained in the region of real object [13]. Also, it is not possible to distinguish two objects having the same color and the different shape, (2) for the mapogram, capability to overcome the affect of noise is not very good.…”

Section: Introductionmentioning

confidence: 99%

“…Further, a modification to the similarity measure for a spatiogram and a Lie group based similarity measure were respectively proposed in [5][6][7]. A different method, mapogram, which incorporates spatial information into a histogram is proposed [13]. In mapogam, the spatial information is contained in 2D probabilistic "maps".…”

Section: Introductionmentioning

confidence: 99%

Moving object tracking based on geogram

Choe

Wang

Liu

et al. 2014

Multimed Tools Appl

View full text Add to dashboard Cite

We introduce the concept of a geogram that captures richer features to represent the objects. The spatiogram contains some moments upon the coordinates of the pixels corresponding to each bin while the geogram contains information about the perimeter of grouped regions in addition to features in the spatiogram. The perimeter of the given region has capability to represent the geometrical compactness on the distribution of the given feature. The geogram-based feature descriptor increases the accuracy of tracking because it can capture the features in lower levels. Also, we consider that a convergence of the mean shift algorithm for the spatiogram is divided into obvious dynamic and steady states as well as in automatic control, and introduce a hybrid technique of the geogram and the histogram to control convergence process. Moreover, we derive a mean shift procedure for the proposed geogram. We test our feature descriptor and measure in object tracking scenario. Experimental results demonstrate that the geogram has promising discriminative capability in comparison with other ones. Electronic supplementary materialThe online version of this article (

show abstract

On histograms and spatiograms - introduction of the mapogram

Cited by 10 publications

References 6 publications

Integration of Mean-Shift and Particle Filter: A Survey

Integration of Mean-Shift and Particle Filter: A Survey

"Mammographic Image Enhancement, Classification and Retrieval using Color, Statistical and Spectral Analysis"

Moving object tracking based on geogram

Contact Info

Product

Resources

About