1. Visual Objects and Data Objects
Chapter 7.
Visual Objects and Data Objects
컴퓨터 교육학과
박사과정 차승은

2. 1. Introduction
For our present purposes, an object can be thought of as any identifiable, separate, and distinct part of the visual world.
Information about visual objects is cognitively stored in a way that ties together critical Features, such as oriented edges and patches of color and texture, so that they can Be identified, visually tracked, and remembered.
→ Because visual objects cognitively group visual attributes, if we can represent data values as visual features and group these features into visual objects, we will have a very powerful tool for organizing related data.

3. Image-based Structured based 1. Introduction
Two theories to explain Object recognition
Image-based
: matching the visual image with a snapshot stored in memory
Structured based
: primitive 3D forms and the structural interrelationships between objects
→ Two radically different theories have been proposed to explain object recognition.

4. 2. Image – based Object Recognition
♪ Experiment 1 :
remarkable ability to recall pictorial images
♪ Experiment 2 :
Rapid Serial Visual Presentation (RSVP)
♪ Experiment 3 :
Attentional blink
♪ Experiment 4 :
Recognition VS recall → icon

5. 2. Image – based Object Recognition

6. 2. Image – based Object Recognition
The term of Priming
: that people can identify objects more easily if they are given prior exposure to some relevant information.
Canonical view
: 많은 각각의 사물들은 사물의 식별이 용이하게 해주는 “Canonical view ”가 존재
→ we recognize objects by matching the visual information with internally stored view point specific examplars, or “prototypes”.
→ According to this theory, the brain stores a number of key views of objects.
→ These views are not simple snapshots; they allow recognition despite simple geometric distortions of the image that occur in perspective transformation.
♪ However, there are strict limits on the extent to which we can change an image before recognition problems occur. ( ex : face recognition upside-down)

7. 2. Image – based Object Recognition

8. 2. Image – based Object Recognition

9. 2. Image – based Object Recognition
Application of Images in User Interface
Icon
: visual images are easily recognized after so little exposure.
icons in user interfaces should make excellent memory aids.
helping us recall the functionality of parts of complex systems.

10. 2. Image – based Object Recognition
Application of Images in User Interface
Priming
: useful in helping people search for particular patterns in data.
The obvious way of doing this is to provide sample images of the kind of pattern being sought and repeating the samples at frequent intervals during the search process.
(ex : using images of sample viruses in medical screening laboratory)

11. 2. Image – based Object Recognition
Application of Images in User Interface
Searching an Image Database
: Presenting images rapidly in sequence may be a useful way to allow users to scan picture data-bases.
The fact that people can search rapidly for an image in a sequence of up to 10 pictures per second suggests that presenting images using RSVP may be efficient.

12. 2. Image – based Object Recognition
Application of Images in User Interface
Searching an Image Database
: Contrast this with the usual method of presenting image collections in a regular grid of small thumbnail images.
If it is necessary to make an eye movement to fixate each thumbnail image, it will not be possible to scan more than three to four images per second.

13. 2. Image – based Object Recognition

14. 2. Image – based Object Recognition

15. 3. Structure – based Object Recognition
Image-based theories of object recognition imply a rather superficial level of analysis of visual objects.
Yet despite the fact that the images of these two objects are very different from
one another, they can be rapidly recognized as representations of the same object.
• No image-based theory can account for this result.

16. 3. Structure – based Object Recognition
Geon Theory
컴퓨터 시각의 또 다른 응용으로 물체 인식이 있다. Biederman [985] 에 따르면 많은 부류의 물체들은 지온 (geon) 이라고 부르는 소수의 기본 구성요소의 집합으로 이루어진다. 그리하여 이러한 지온에 기반을 둔 시스템들이 고안되었으나 아직까지는 제한된 부류의 물체 인식에만 사용되었다.

17. 3. Structure – based Object Recognition
Geon Theory

18. 3. Structure – based Object Recognition
Geon Theory
somewhat simplified overview of a neural-network model of structural
object perception, developed by Hammel and Biederman (1992).

19. 3. Structure – based Object Recognition
Silhouettes
• Silhouettes appear to be especially important in determining how we perceive the structure of objects.
• The fact that simplified line drawings are often silhouettes may, in part, account for our ability to interpret them.
• At some level of perceptual processing, the silhouette boundaries of objects and the simplified line drawings of those objects excite the same neural contour-extraction mechanisms.

20. 3. Structure – based Object Recognition
Silhouettes
• Many objects have particular silhouettes that are easily recognizable; think of a teapot, a shoe, a church, a person, or a violin.
• These canonical silhouettes are based on a particular view of an object, often from a point at right angles to a major plane of symmetry.

21. 3. Structure – based Object Recognition
Silhouettes
• Certain simplified views should be easier to read.
• Time is required for detailed information to be perceived.
• Simplified line drawings may be most appropriate only when rapid responses are required.

22. 4. Faces • Faces are special objects in human perception.
• Infants learn about faces faster than other objects.
• It is as if we are born with visual systems primed to learn to recognize important humans, such as our own mothers.
• A specific area of our brains, the right middle fusiform gyrus, is especially important in face perception.
(A) (B)

23. 4. Faces
• This area is also useful for recognizing other complex objects, such as automobiles.
• Faces have an obvious importance in communication.
• Certain human expressions are universal communication signals, correctly interpreted across cultures and social groups (Ekman and Friesen, 1975).

24. 4. Faces
• Ekman identified six universal expressions: happiness, anger, sadness, disgust, fear, surprise.
• The motion of facial features is also important in conveying emotion.
• Animated images are necessary to convey a full range of nuanced emotion; it is especially important to show motion of the eyebrows.
행복함 신남 화남 슬픔
역겨움 결심 두려움 놀람

25. 5. Object Display and Object-Based Diagrams
• Wickens (1992) is primarily responsible for the concept of an object display as a graphical device employing “a single
contoured object” to integrate a large
number of separate variables.
• Wickens theorized that mapping many
data variables onto a single object will
guarantee that these variables are
processed together, in parallel.
• Among the earlier examples of object
displays are Chernoff faces, named after
their inventor, Herman Chernoff (1973).
• Faces are probably the most important
class of objects in the human environment.

26. 5. Object Display and Object-Based Diagrams
• advantages It can reduce accidental misreadings of data values.
• disadvantage They lack generality.

27. 5. Object Display and Object-Based Diagrams
Geon diagram
• Biederman’s geon theory, outlined earlier, can be applied directly to object display design.
• If cylinders and cones are indeed perceptual primitives, it will make sense to construct diagrams using these geon elements.
• Geons are used to represent the major components of a compound data object, whereas the architecture of the data object is represented by the structural skeleton linking the geons.
• The size of a geon becomes a natural metaphor for the relative importance of a data entity, or its complexity or relative value.

28. 5. Object Display and Object-Based Diagrams
Geon diagram

29. 5. Object Display and Object-Based Diagrams
Geon diagram

30. 6. Perceiving the Surface Shape of Objects
Spatial Cues for Representing Scalar Fields
• Use computer graphics techniques to shade the data surface with a simulated light source and give it a simulated color and texture to make it look like a real physical surface.
• Such a simulated surface can be viewed using a stereoscopic viewing apparatus, by creating different perspective images, one for each eye.

31. 6. Perceiving the Surface Shape of Objects
Spatial Cues for Representing Scalar Fields
• An important issue in the creation of univariate maps is determining how to represent surface shape most effectively.
• Four principal sets of visual cues for surface shape perception :
shading models
surface texture
stereoscopic depth
motion parallax.

32. 6. Perceiving the Surface Shape of Objects
Shading Models
• The basic shading model used in computer graphics to represent the interaction of light with surfaces has already been discussed in Chapter 2. 1
Lambertian shading
Light reflected from a surface equally in all directions 화소의 밝기 값이 광원과 영상 내 표면의 모양에 의존한다고 가정한다. 그러나 실제 Lamertian 모델을 따르지 않는 경우가 많아서 이를 개선하기 위한 연구가 많이 이루어지고 있다. 2
Specular shading
The highlights reflected from a glossy surface 3
Ambient shading
Light coming from the surrounding environment 4
Cast shadows
Shadows cast by an object, either on itself or on other objects (일사광선이 불투명한 물체를 통과하지 못하여 생기는 그림자 )

33. 6. Perceiving the Surface Shape of Objects
Shading Models

34. 6. Perceiving the Surface Shape of Objects
Shading Models

35. 6. Perceiving the Surface Shape of Objects
Shading Models

36. 6. Perceiving the Surface Shape of Objects
Surface Texture
물론, 이 표면결소는 매우 불규칙적으로 배열될 것이다. 그러나, 우리는 평면의 각 단위 면적이 거의 동일한 수의 표면결소를 포함한다는 의미에서 표면결소들은 균등배열된다고 가정한다. 그러나, 영상에서 표면결소 밀도는 균등하지 않을 것이며 위치적으로는 다양하다. 그러면 영상의 표면결 밀도의 기울기 (최대 변화율의 크기와 방향) 는 표면 방향을 결정하고 크기는 표면 기울기 (surface slant) 와 경사 방향 (direction of the tilt) 에 의해서 정해진다.
Texture로부터의 형상화 문제에 대한 첫 번째 시도자는 Gibson [1979] 이었다. 인간이 어떻게 Texture로부터 표면 방향을 인지하는가를 이론화시키려 시도를 한 결과, 그는 Texture이 표면결소 (texel) 라고 불리는 작은 것으로 이루어진다고 가정하였다.

37. 6. Perceiving the Surface Shape of Objects
motion parallax
관찰자가 대상을 보면서 움직이면 가까운 대상은 먼 대상보다 더 많이 눈의 망막상에서 옮겨지는데 이를 운동시차라 한다. (motion parallax).
기차를 타고 가다 보면 먼 산은 자기를 따라오고 가까운 전봇대는 빠르게 뒤로 가는 것처럼 보이는 것이 그 예이다.
움직이는 물체의 경우 그 이동속도는 원근감에 영향을 준다. 두 점의 깊이를 결정할 때에는, 그들이 상대적으로 얼마만큼 움직였는가를 관찰한다. 머리를 왼쪽에서 오른쪽으로 또는 위쪽에서 아래쪽으로 움직이면 가까이 있는 점들이 다른 점들보다 더 많이 움직인 것처럼 보인다.
이것을 주변시각능력이라고 부른다. 따라서 머리를 움직이게 되면 장면에 대해 서로 다른 시점들이 생기게 된다.

38. 6. Perceiving the Surface Shape of Objects
Integration of Cues for Surface Shape
Surface 인식에 관해 많은 요인들이 존재하지만 어떤 것이 가장 효과적으로 작용하는가에 관해 실험
-> texture와 shading이 없다면 stereo와 motion은 당연히 효과가 없기 때문에 stereo와 motion의 조합에 대해서 실험
운동 단서.
관찰자가 대상을 보면서 움직이면 가까운 대상은 먼 대상보다 더 많이 눈의 망막상에서 옮겨지는데 이를 운동시차라 한다. (motion parallax).
기차를 타고 가다보면 먼 산은 자기를 따라오고 가까운 전봇대는 빠르게 뒤로 가는 것처럼 보이는 것이 그 예이다.
서로 겹쳐져 보이는 두 대상을 보면서 움직일 때는 방향에 따라 뒤쪽 대상의 표면에서 삭제와증식이 일어난다. (deletion and accretion).
이 단서는 중첩과 운동시차가 결합된 것으로서 모서리에서의 상대적 거리를 정확히 판단하는데 유용하다.

39. 6. Perceiving the Surface Shape of Objects
each visual task, there is mainly effective factor.

40. 6. Perceiving the Surface Shape of Objects
Interaction of Shading and Contour

41. 6. Perceiving the Surface Shape of Objects
Interaction of Shading and Contour

42. 6. Perceiving the Surface Shape of Objects
Bivariate Maps : Lighting and Surface Color

43. 7.Integration
In this chapter, object 인지를 위해 구조와 표현과 상호작용하는 각기 다른 spatial variables를 다루는 법을 소개했다.
However, 뇌가 어떻게 다른 정보들을 구조화하는 지에 대해서는 논의하고 있지 않다.
Unfortunately, 아직 명확한 답은 없다. 매우 추론적인 이론만이 존재한다.

44. Thank you!!