The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini mencadangkan metodologi untuk mengesan objek permukaan matte pada pemandangan menggunakan maklumat warna dan ambang spatial. Pertama, imej perbezaan diperoleh melalui perbandingan piksel-bijak kandungan warna imej rujukan 'bersih' dan imej sampel. Kemudian, ambang spatial bagi imej perbezaan dilakukan untuk mengekstrak sebarang objek yang menarik, diikuti dengan pemprosesan pasca morfologi untuk membuang hingar piksel. Kami mengkaji kebolehgunaan dua ruang warna ganti (HSV, CIE Lab) untuk mengira imej perbezaan. Begitu juga, kami menggunakan dua kaedah ambang spatial, yang menentukan ambang global daripada sifat spatial tempatan imej perbezaan. Kami menunjukkan prestasi pendekatan yang dicadangkan dalam pengawasan tempat kejadian, di mana objektifnya adalah untuk memantau dok perkapalan untuk kemunculan objek tidak perlu seperti kotak kadbod. Untuk menganalisis keteguhan pendekatan, eksperimen termasuk tiga jenis pemandangan berbeza yang dikategorikan sebagai 'mudah,' 'sederhana' dan 'sukar' berdasarkan sifat seperti kepelbagaian latar belakang, kewujudan bayang-bayang dan perubahan pencahayaan, dan sifat pemantulan dan kroma objek. Keputusan eksperimen menunjukkan bahawa ketepatan pengecaman yang agak baik dicapai pada adegan 'mudah' dan 'sederhana', manakala adegan 'sukar' kekal sebagai cabaran untuk kerja masa hadapan.
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Salinan
Mika RAUTIAINEN, Timo OJALA, Hannu KAUNISKANGAS, "Detecting Perceptual Color Changes from Sequential Images for Scene Surveillance" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 12, pp. 1676-1683, December 2001, doi: .
Abstract: This paper proposes a methodology for detecting matte-surfaced objects on a scene using color information and spatial thresholding. First, a difference image is obtained via a pixel-wise comparison of the color content of a 'clean' reference image and a sample image. Then, spatial thresholding of the difference image is performed to extract any objects of interest, followed by morphological post-processing to remove pixel noise. We study the applicability of two alternate color spaces (HSV, CIE Lab) for computing the difference image. Similarly, we employ two spatial thresholding methods, which determine the global threshold from the local spatial properties of the difference image. We demonstrate the performance of the proposed approach in scene surveillance, where the objective is to monitor a shipping dock for the appearance of needless objects such as cardboard boxes. In order to analyze the robustness of the approach, the experiment includes three different types of scenes categorized as 'easy,' 'moderate,' and 'difficult,' based on properties such as heterogeneity of the background, existence of shadows and illumination changes, and reflectivity and chroma properties of the objects. The experimental results show that relatively good recognition accuracy is achieved on 'easy' and 'moderate' scenes, whereas 'difficult' scenes remain a challenge for future work.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_12_1676/_p
Salinan
@ARTICLE{e84-d_12_1676,
author={Mika RAUTIAINEN, Timo OJALA, Hannu KAUNISKANGAS, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Perceptual Color Changes from Sequential Images for Scene Surveillance},
year={2001},
volume={E84-D},
number={12},
pages={1676-1683},
abstract={This paper proposes a methodology for detecting matte-surfaced objects on a scene using color information and spatial thresholding. First, a difference image is obtained via a pixel-wise comparison of the color content of a 'clean' reference image and a sample image. Then, spatial thresholding of the difference image is performed to extract any objects of interest, followed by morphological post-processing to remove pixel noise. We study the applicability of two alternate color spaces (HSV, CIE Lab) for computing the difference image. Similarly, we employ two spatial thresholding methods, which determine the global threshold from the local spatial properties of the difference image. We demonstrate the performance of the proposed approach in scene surveillance, where the objective is to monitor a shipping dock for the appearance of needless objects such as cardboard boxes. In order to analyze the robustness of the approach, the experiment includes three different types of scenes categorized as 'easy,' 'moderate,' and 'difficult,' based on properties such as heterogeneity of the background, existence of shadows and illumination changes, and reflectivity and chroma properties of the objects. The experimental results show that relatively good recognition accuracy is achieved on 'easy' and 'moderate' scenes, whereas 'difficult' scenes remain a challenge for future work.},
keywords={},
doi={},
ISSN={},
month={December},}
Salinan
TY - JOUR
TI - Detecting Perceptual Color Changes from Sequential Images for Scene Surveillance
T2 - IEICE TRANSACTIONS on Information
SP - 1676
EP - 1683
AU - Mika RAUTIAINEN
AU - Timo OJALA
AU - Hannu KAUNISKANGAS
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2001
AB - This paper proposes a methodology for detecting matte-surfaced objects on a scene using color information and spatial thresholding. First, a difference image is obtained via a pixel-wise comparison of the color content of a 'clean' reference image and a sample image. Then, spatial thresholding of the difference image is performed to extract any objects of interest, followed by morphological post-processing to remove pixel noise. We study the applicability of two alternate color spaces (HSV, CIE Lab) for computing the difference image. Similarly, we employ two spatial thresholding methods, which determine the global threshold from the local spatial properties of the difference image. We demonstrate the performance of the proposed approach in scene surveillance, where the objective is to monitor a shipping dock for the appearance of needless objects such as cardboard boxes. In order to analyze the robustness of the approach, the experiment includes three different types of scenes categorized as 'easy,' 'moderate,' and 'difficult,' based on properties such as heterogeneity of the background, existence of shadows and illumination changes, and reflectivity and chroma properties of the objects. The experimental results show that relatively good recognition accuracy is achieved on 'easy' and 'moderate' scenes, whereas 'difficult' scenes remain a challenge for future work.
ER -