scholarly journals KLASIFIKASI OBJEK BERDASARKAN WARNA, BENTUK DAN DIMENSI

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Budi Sugandi ◽  
Aprianto Riwan Doni ◽  
Dwi Ayu Imardiyanti
Keyword(s):  
Shape Matching ◽  
Color Histogram ◽  
Reference Object ◽  
Conveyor System ◽  
Color Dimension ◽  
Sorting Machine ◽  
Object Based ◽  
Combined Features ◽  
Object Feature ◽  
Histogram Shape

ABSTRACTThis research aimed to develop an algorithm to classify an object based on color, shape and dimension. In this research, the classification process was designed on two combined features which are color-shape, color-dimension, and shape-dimension. Each two combined features yielded 9 classification results. Therefore, in total, the number of classifications of three combined features were 27 classifications. The algorithm was depeloved as follows. The color feature was classiffied using RGB color histogram. Each object histogram was compared and calculated the distance to the reference object histogram. The similarity was determined with the smaller distance to the reference object. The shape feature was classified using shape matching algorihtm. The algorithm measured the similarity based on distance between two point on each geometry. The dimension feature was classified using the number of the pixel on each object. The number of pixel was callibrated on certain distance to the camera. The algoritma was also designed to classify the reject object (Not Good/NG). The algortihm was implemented on a conveyor system as a sorting machine. The conveyor has 10 output classification, 9 output for object classification and 1 output for NG object. The experimental results showed the sistem could classify the object in 27 category classification and 1 NG category.Keywords: object cleassification, object feature, RGB color histogram, shape matching, object sortingABSTRAKPenelitian ini bertujuan untuk membangun algoritma untuk pengklasifikasian objek berdasarkan warna, bentuk dan dimensi. Dalam penelitian ini, pengklasifikasian didesain dalam dua ciri gabungan yaitu warna-bentuk, warna-dimensi dan bentuk-dimensi. Tiap-tiap dua ciri gabungan tersebut akan menghasilkan 9 hasil klasifikasi. Sehingga total klasifikasi gabungan 3 ciri adalah 27 klasifikasi. Algoritma yang dikembangkan untuk masing-masing ciri adalah sebagai berikut. Ciri warna diklasifikasikan menggunakan histogram warna RGB. Tiap objek dibandingkan histogramnya dan dihitung jarak tiap histogram dengan histogram referensi. Semakin dekat dengan histogram referensi, maka objek tersebut diklasifikasikan sama dengan objek referensi. Ciri bentuk diklasifikasikan menggunakan algoritma shape matching. Algoritma shape matching mengukur kemiripan objek berdasarkan jarak antar titik dalam objek geometri. Semakin rendah perbedaannya menunjukkan semakin mirip objek tersebut. Sementara ciri dimensi diklasifikasikan menggunakan perhitungan jumlah pixel pada tiap objek. Jumlah pixel dikalibrasi pada jarak tertentu dari pengkapturan objek oleh kamera. Selain itu, algoritma ini pun didesain untuk dapat mengklasifikasi objek yang tidak masuk kategori dan diklasifikasikan sebagai objek yang rusak (Not Good/NG). Algoritma ini selanjutnya diimplemenasikan dalam sebuah konveyor sebagai mesih pemisah objek yang mempunyai 10 keluaran. Sembilan keluaran untuk objek hasil klasifikasi dan satu keluaran untuk objek dalam kategori NG. Hasil eksperimen menunjukkan sistem dapat mengklasifikasi objek dalam 27 kategori dan 1 kategori NG.Kata Kunci: klasifikasi objek, ciri objek, histogram warna RGB, shape mathing, pemilah objek

scholarly journals Object-based selection in visual working memory

2021 ◽  
Author(s):  
Yin-ting Lin ◽  
Garry Kong ◽  
Daryl Fougnie
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Spatial Distance ◽  
Object Selection ◽  
Object Based ◽  
Memory Experiment ◽  
Object Feature ◽  
Feature Dimension ◽  
Selection Of ◽  
Attentional Mechanisms

AbstractAttentional mechanisms in perception can operate over locations, features, or objects. However, people direct attention not only towards information in the external world, but also to information maintained in working memory. To what extent do perception and memory draw on similar selection properties? Here we examined whether principles of object-based attention can also hold true in visual working memory. Experiment 1 examined whether object structure guides selection independently of spatial distance. In a memory updating task, participants encoded two rectangular bars with colored ends before updating two colors during maintenance. Memory updates were faster for two equidistant colors on the same object than on different objects. Experiment 2 examined whether selection of a single object feature spreads to other features within the same object. Participants memorized two sequentially presented Gabors, and a retro-cue indicated which object and feature dimension (color or orientation) would be most relevant to the memory test. We found stronger effects of object selection than feature selection: accuracy was higher for the uncued feature in the same object than the cued feature in the other object. Together these findings demonstrate effects of object-based attention on visual working memory, at least when object-based representations are encouraged, and suggest shared attentional mechanisms across perception and memory.

scholarly journals Digital Image Processing based Surface Area Calculation

2020 ◽  
Vol 10 (1) ◽  
pp. 186-190
Keyword(s):  
Image Processing ◽  
Surface Area ◽  
Digital Image Processing ◽  
Digital Image ◽  
Reference Object ◽  
Nondestructive Measurement ◽  
Actual Surface ◽  
Regular Shape ◽  
Object Based ◽  
Calculation System

The objective of current work is nondestructive measurement of surface area of regular or irregular shape just from image. Surface area calculation is mathematical part which needs to remember number of formulas and all for regular shape. It becomes more tedious if the shape whose area is to be calculated is irregular. In some cases such as mountain or lake measurement of dimension is also cumbersome task. To find the solution for such cases in today’s world of automation, the proposed work describes reference object based area calculation system which is based on different techniques of digital image processing. In this we have to click an image of object (whose area is to be calculated) along with one reference object with known surface area. Then the proposed system will perform image enhancement and segmentation operation and finally calculate the surface area of any 2-D surface. It is observed that the values obtained are having with good correlation with actual surface area values.

scholarly journals Selective neural coding of object, feature, and geometry spatial cues in humans

2021 ◽  
Author(s):  
Stephen Ramanoël ◽  
Marion Durteste ◽  
Alice Bizeul ◽  
Anthony Ozier-Lafontaine ◽  
Marcia Bécu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spatial Coding ◽  
Spatial Cues ◽  
Human Navigation ◽  
Visual Spatial ◽  
Object Based ◽  
Feature Based ◽  
The Neural Networks ◽  
Object Feature ◽  
Neural Signatures

SummaryOrienting in space requires the processing and encoding of visual spatial cues. The dominant hypothesis about the brain structures mediating the coding of spatial cues stipulates the existence of a hippocampal-dependent system for the representation of geometry and a striatal-dependent system for the representation of landmarks. However, this dual-system hypothesis is based on paradigms that presented spatial cues conveying either conflicting or ambiguous spatial information and that amalgamated the concept of landmark into both discrete 3D objects and wall features. These confounded designs introduce difficulties in interpreting the spatial learning process. Here, we test the hypothesis of a complex interaction between the hippocampus and the striatum during landmark and geometry visual coding in humans. We also postulate that object-based and feature-based navigation are not equivalent instances of landmark-based navigation as currently considered in human spatial cognition. We examined the neural networks associated with geometry-, object-, and feature-based spatial navigation in an unbiased, two-choice behavioral paradigm using fMRI. We showed evidence of a synergistic interaction between hippocampal and striatal coding underlying flexible navigation behavior. The hippocampus was involved in all three types of cue-based navigation, whereas the striatum was more strongly recruited in the presence of geometric cues than object or feature cues. We also found that unique, specific neural signatures were associated with each spatial cue. Critically, object-based navigation elicited a widespread pattern of activity in temporal and occipital regions relative to feature-based navigation. These findings challenge and extend the current view of a dual, juxtaposed hippocampal-striatal system for visual spatial coding in humans. They also provide novel insights into the neural networks mediating object vs. feature spatial coding, suggesting a need to distinguish these two types of landmarks in the context of human navigation.HighlightsComplex hippocampal-striatal interaction during visual spatial coding for flexible human navigation behavior.Distinct neural signatures associated with object-, feature-, and geometry-based navigation.Object- and feature-based navigation are not equivalent instances of landmark-based navigation.

Retaining event files in working memory requires extra object-based attention than the constituent elements

2019 ◽  
Vol 72 (9) ◽  
pp. 2225-2239 ◽  
Author(s):  
Xiqian Lu ◽  
Xiaochi Ma ◽  
Yangfan Zhao ◽  
Zaifeng Gao ◽  
Mowei Shen
Keyword(s):  
Working Memory ◽  
Visual Processing ◽  
Secondary Task ◽  
Maintenance Phase ◽  
Action Planning ◽  
Related Information ◽  
Object Based ◽  
Event Files ◽  
Object Feature ◽  
Constituent Elements

Retaining events containing action-related information in working memory (WM) is vital to daily activities such as action planning and social interaction. During processing of such events, action-related information is bound with other visual elements (e.g., colours) as event files. In this study, we explored whether retaining event files in WM consumes more attention than retaining the constituent elements. Considering that object-based attention underlies the rehearsal of static feature bindings in WM, we hypothesised that object-based attention played a key role in retaining event files in WM. As biological motion (BM) is one of the most frequently observed events in daily life, we employed BM-related event files as the tested stimuli. In separate blocks, we required participants to memorise BM, colours (or locations), or the binding between these elements (i.e., event files). Critically, we added an object-feature report task, which consumed object-based attention, during the WM maintenance phase. We predicted that the added secondary task would lead to larger impairment for BM event files than for the constituent elements. In line with this prediction, Experiments 1 and 2 consistently revealed a selective impairment to BM event files, which could not be attributed to an unbalanced number of elements between memory conditions (Experiment 3), or to the visual processing of a secondary task (Experiment 4). Taken together, these results suggest that object-based attention plays a pivotal role in maintaining event files in WM.

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