The document discusses perception in artificial intelligence. It defines perception as acquiring, interpreting, and organizing sensory information. Perception involves both sensation, where sensors convert signals into data, and higher-level processes that make sense of the data. The document then discusses challenges in perception like abstraction and uncertainty in relations. It also notes perception is influenced by both internal and external factors beyond just signals.

1. Prof.Mrs.M.P.Atre
PVG’s COET, SPPU
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2.  Perception is the process of acquiring,
interpreting, selecting, and organizing
sensory information.
 Perception presumes sensation, where
various types of sensors each converts a
certain type of simple signal into data of the
system.
 To put the data together and to make sense
out of them is the job of the perception
mechanism.
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3.  Perception can be seen as a special type
of categorization (or classification, pattern
recognition) where the inputs are sensory
data, and the outputs are categorical
judgments and conceptual relations.
 The difficulty of the task comes from the
need of multiple levels of abstraction, where
the relations among data items are many-to-
many, uncertain, and changing over time.
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4.  Accurately speaking, we never "see things as
they are", and perception process of an
intelligent system is often (and should be)
influenced by internal and external factors
beside the signals themselves.
 Furthermore, perception is not a pure
passive process driven by the input.
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5.  In AI, the study on perception is mostly
focused on the reproduction of human
perception, especially on the perception of
aural and visual signals.
 However, this is not necessarily the case
since the perception mechanism of a
computer system does not have to be
identical to that of a human being.
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Hearing
Vision

7.  Speech recognition is the front-end of a
system that can perceive and understand
spoken language, as used in voice command
interface and speech-to-speech translation.
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8. 9/18/2017 8
acoustic-phonetic
approach
pattern-matching
approach
artificial intelligence
approach

9.  postulates that there exist finite, distinctive
phonetic units (phonemes) in spoken
language and that these units are broadly
characterized by a set of acoustic properties
 Even though the acoustic properties of
phonetic units are highly variable, both with
speakers and with neighboring sounds, it is
assumed in the acoustic-phonetic approach
that the rules governing the variability are
straightforward and can be readily learned
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10.  represents a speech-pattern in the form of a
mathematical model
 A direct comparison is made between the
unknown speech (the speech to be
recognized) with each possible pattern
learned in the training stage in order to
determine the identity of the unknown
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11.  attempts to do speech recognition using
various AI techniques, such as knowledge-
based systems or neural networks
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12.  translation from text to speech
 After the text analysis capabilities pre-
process the text (digit sequences,
abbreviations, etc.) the pronunciations of
most ordinary words and proper names are
decided by the dictionary-based methods
 Finally there are methods responsible for
post-processing (prosodic phrasing, word
accentuation, sentence intonation) and the
actual speech synthesis
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13.  A major remaining problem is naturalness,
especially context and meaning related
adjustments (emotion, stress, tone, ...)
 To fully solve this problem, it is probably
necessary to fully understand the meaning of
the message and the purpose of the speech
 Music perception and composition are also
studied in AI. For example, there are music
works produced by a computer program, and
some of them are in the styles of various
classical composers
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14.  vision begins with a large array of
measurements of the light reflected from
object surfaces onto the eye
 Analysis then proceeds in multiple stages,
with each producing increasingly more useful
representations of information in the scene
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15.  Stage 1
 Early representations may capture information
such as the location, contrast, and sharpness of
significant intensity changes or edges in the
image.
 Such changes correspond to physical features
such as object boundaries, texture contours, and
markings on object surfaces, shadow boundaries,
and highlights.
 In the case of a dynamically changing scene, the
early representations may also describe the
direction and speed of movement of image
intensity changes.
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16.  Stage 2
 Intermediate representations describe
information about the three-dimensional (3-
D) shape of object surfaces from the
perspective of the viewer, such as the
orientation of small surface regions or the
distance to surface points from the eye
 Such representations may also describe the
motion of surface features in three
dimensions.
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17.  Stage 3
 Higher-level representations of objects
describe their 3-D shape, form, and
orientation relative to a coordinate frame
based on the objects or on a fixed location in
the world
 Tasks such as object recognition, object
manipulation, and navigation may operate
from the intermediate or higher-level
representations of the 3-D layout of objects
in the world.
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18.  For relatively simple pattern
recognition problems, neural network is
often used to directly map input into output
via a learning process.
 In recent years, hierarchical learning
methods have made remarkable progresses
on various problems, such as CAPTCHA.
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19.  Vision is not a pure input process.
 Eye movement has important impact on
human visual perception
 An active vision system is one that is able to
interact with its environment by altering its
viewpoint rather than passively observing it,
and by operating on sequences of images
rather than on a single frame.
 Also, there is some study on using the eye-
gaze of a computer user in the interface to
aid the control of the application.
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20.  By "higher-level perception", we mean how
the given input data is categorized
 While in low-level perception, the processing
is mostly "bottom-up", i.e., the output is
more or less a function of the input, in
higher-level perception there are many more
factors involved.
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21.  "One of the most important properties of high-
level perception is that it is extremely flexible.
 A given set of input data may be perceived in a
number of different ways, depending on the
context and the state of the perceiver
 Due to this flexibility, it is a mistake to regard
perception as a process that associates a fixed
representation with a particular situation.
 Both contextual factors and top-down cognitive
influences make the process far less rigid than
this."
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22.  Letter Spirit
 The style of a composer
 Cartoon creation and comprehension
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23. https://cis.temple.edu/~wangp/3203-
AI/Lecture/IO-2.htm
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